added openai in orchestrator
Browse files- gemini_report_generator.py +1184 -316
gemini_report_generator.py
CHANGED
@@ -1,366 +1,1234 @@
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import json
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import numpy as np
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import pandas as pd
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import re
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import os
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import uuid
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import logging
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from io import StringIO
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import sys
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import traceback
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from typing import Optional, Dict, Any, List
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from pydantic import BaseModel, Field
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from google.generativeai import GenerativeModel, configure
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from dotenv import load_dotenv
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import seaborn as sns
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import datetime as dt
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from supabase_service import upload_file_to_supabase
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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pd.set_option('display.max_colwidth', None)
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load_dotenv()
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API_KEYS = os.getenv("GEMINI_API_KEYS", "").split(",")[::-1]
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MODEL_NAME = 'gemini-2.0-flash'
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class FileProps(BaseModel):
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class Files(BaseModel):
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class FileBoxProps(BaseModel):
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os.environ['MPLBACKEND'] = 'agg'
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import matplotlib.pyplot as plt
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plt.show = lambda: None
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logging.basicConfig(
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)
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logger = logging.getLogger(__name__)
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class GeminiKeyManager:
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class PythonREPL:
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import matplotlib.pyplot as plt
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plt.switch_backend('agg')
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{code}
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plt.close('all')
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class RethinkAgent(BaseModel):
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def gemini_llm_chat(csv_url: str, query: str) -> Dict[str, Any]:
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async def generate_csv_report(csv_url: str, query: str) -> FileBoxProps:
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raise ValueError("Unexpected response format from gemini_llm_chat")
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360 |
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361 |
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362 |
|
363 |
-
#
|
364 |
-
#
|
365 |
-
#
|
366 |
-
#
|
|
|
1 |
+
# import json
|
2 |
+
# import numpy as np
|
3 |
+
# import pandas as pd
|
4 |
+
# import re
|
5 |
+
# import os
|
6 |
+
# import uuid
|
7 |
+
# import logging
|
8 |
+
# from io import StringIO
|
9 |
+
# import sys
|
10 |
+
# import traceback
|
11 |
+
# from typing import Optional, Dict, Any, List
|
12 |
+
# from pydantic import BaseModel, Field
|
13 |
+
# from google.generativeai import GenerativeModel, configure
|
14 |
+
# from dotenv import load_dotenv
|
15 |
+
# import seaborn as sns
|
16 |
+
# import datetime as dt
|
17 |
|
18 |
+
# from supabase_service import upload_file_to_supabase
|
19 |
|
20 |
+
# pd.set_option('display.max_columns', None)
|
21 |
+
# pd.set_option('display.max_rows', None)
|
22 |
+
# pd.set_option('display.max_colwidth', None)
|
23 |
|
24 |
+
# load_dotenv()
|
25 |
|
26 |
|
27 |
+
# API_KEYS = os.getenv("GEMINI_API_KEYS", "").split(",")[::-1]
|
28 |
+
# MODEL_NAME = 'gemini-2.0-flash'
|
29 |
|
30 |
+
# class FileProps(BaseModel):
|
31 |
+
# fileName: str
|
32 |
+
# filePath: str
|
33 |
+
# fileType: str # 'csv' | 'image'
|
34 |
|
35 |
+
# class Files(BaseModel):
|
36 |
+
# csv_files: List[FileProps]
|
37 |
+
# image_files: List[FileProps]
|
38 |
|
39 |
+
# class FileBoxProps(BaseModel):
|
40 |
+
# files: Files
|
41 |
|
42 |
+
# os.environ['MPLBACKEND'] = 'agg'
|
43 |
+
# import matplotlib.pyplot as plt
|
44 |
+
# plt.show = lambda: None
|
45 |
|
46 |
+
# logging.basicConfig(
|
47 |
+
# level=logging.INFO,
|
48 |
+
# format='%(asctime)s - %(levelname)s - %(message)s'
|
49 |
+
# )
|
50 |
+
# logger = logging.getLogger(__name__)
|
51 |
|
52 |
+
# class GeminiKeyManager:
|
53 |
+
# """Manage multiple Gemini API keys with failover"""
|
54 |
|
55 |
+
# def __init__(self, api_keys: List[str]):
|
56 |
+
# self.original_keys = api_keys.copy()
|
57 |
+
# self.available_keys = api_keys.copy()
|
58 |
+
# self.active_key = None
|
59 |
+
# self.failed_keys = {}
|
60 |
|
61 |
+
# def configure(self) -> bool:
|
62 |
+
# while self.available_keys:
|
63 |
+
# key = self.available_keys.pop(0)
|
64 |
+
# try:
|
65 |
+
# configure(api_key=key)
|
66 |
+
# self.active_key = key
|
67 |
+
# logger.info(f"Configured with key: {self._mask_key(key)}")
|
68 |
+
# return True
|
69 |
+
# except Exception as e:
|
70 |
+
# self.failed_keys[key] = str(e)
|
71 |
+
# logger.error(f"Key failed: {self._mask_key(key)}. Error: {str(e)}")
|
72 |
+
# logger.critical("All API keys failed")
|
73 |
+
# return False
|
74 |
|
75 |
+
# def _mask_key(self, key: str) -> str:
|
76 |
+
# return f"{key[:8]}...{key[-4:]}" if key else ""
|
77 |
|
78 |
+
# class PythonREPL:
|
79 |
+
# """Secure Python REPL with file generation tracking"""
|
80 |
|
81 |
+
# def __init__(self, df: pd.DataFrame):
|
82 |
+
# self.df = df
|
83 |
+
# self.output_dir = os.path.abspath(f'generated_outputs/{uuid.uuid4()}')
|
84 |
+
# os.makedirs(self.output_dir, exist_ok=True)
|
85 |
+
# self.local_env = {
|
86 |
+
# "pd": pd,
|
87 |
+
# "df": self.df.copy(),
|
88 |
+
# "plt": plt,
|
89 |
+
# "os": os,
|
90 |
+
# "uuid": uuid,
|
91 |
+
# "sns": sns,
|
92 |
+
# "json": json,
|
93 |
+
# "dt": dt,
|
94 |
+
# "output_dir": self.output_dir
|
95 |
+
# }
|
96 |
|
97 |
+
# def execute(self, code: str) -> Dict[str, Any]:
|
98 |
+
# print('Executing code...', code)
|
99 |
+
# old_stdout = sys.stdout
|
100 |
+
# sys.stdout = mystdout = StringIO()
|
101 |
+
# file_tracker = {
|
102 |
+
# 'csv_files': set(),
|
103 |
+
# 'image_files': set()
|
104 |
+
# }
|
105 |
|
106 |
+
# try:
|
107 |
+
# code = f"""
|
108 |
+
# import matplotlib.pyplot as plt
|
109 |
+
# plt.switch_backend('agg')
|
110 |
+
# {code}
|
111 |
+
# plt.close('all')
|
112 |
+
# """
|
113 |
+
# exec(code, self.local_env)
|
114 |
+
# self.df = self.local_env.get('df', self.df)
|
115 |
|
116 |
+
# # Track generated files
|
117 |
+
# for fname in os.listdir(self.output_dir):
|
118 |
+
# if fname.endswith('.csv'):
|
119 |
+
# file_tracker['csv_files'].add(fname)
|
120 |
+
# elif fname.lower().endswith(('.png', '.jpg', '.jpeg')):
|
121 |
+
# file_tracker['image_files'].add(fname)
|
122 |
|
123 |
+
# error = False
|
124 |
+
# except Exception as e:
|
125 |
+
# error_msg = traceback.format_exc()
|
126 |
+
# error = True
|
127 |
+
# finally:
|
128 |
+
# sys.stdout = old_stdout
|
129 |
|
130 |
+
# return {
|
131 |
+
# "output": mystdout.getvalue(),
|
132 |
+
# "error": error,
|
133 |
+
# "error_message": error_msg if error else None,
|
134 |
+
# "df": self.local_env.get('df', self.df),
|
135 |
+
# "output_dir": self.output_dir,
|
136 |
+
# "files": {
|
137 |
+
# "csv": [os.path.join(self.output_dir, f) for f in file_tracker['csv_files']],
|
138 |
+
# "images": [os.path.join(self.output_dir, f) for f in file_tracker['image_files']]
|
139 |
+
# }
|
140 |
+
# }
|
141 |
+
|
142 |
+
# class RethinkAgent(BaseModel):
|
143 |
+
# df: pd.DataFrame
|
144 |
+
# max_retries: int = Field(default=5, ge=1)
|
145 |
+
# gemini_model: Optional[GenerativeModel] = None
|
146 |
+
# current_retry: int = Field(default=0, ge=0)
|
147 |
+
# repl: Optional[PythonREPL] = None
|
148 |
+
# key_manager: Optional[GeminiKeyManager] = None
|
149 |
|
150 |
+
# class Config:
|
151 |
+
# arbitrary_types_allowed = True
|
152 |
|
153 |
+
# def _extract_code(self, response: str) -> str:
|
154 |
+
# code_match = re.search(r'```python(.*?)```', response, re.DOTALL)
|
155 |
+
# return code_match.group(1).strip() if code_match else response.strip()
|
156 |
|
157 |
+
# def _generate_initial_prompt(self, query: str) -> str:
|
158 |
+
# return f"""Generate DIRECT EXECUTION CODE (no functions, no explanations) following STRICT RULES:
|
159 |
|
160 |
+
# MANDATORY REQUIREMENTS:
|
161 |
+
# 1. Operate directly on existing 'df' variable
|
162 |
+
# 2. Save ALL final DataFrames to CSV using: df.to_csv(f'{{output_dir}}/descriptive_name.csv')
|
163 |
+
# 3. For visualizations: plt.savefig(f'{{output_dir}}/chart_name.png')
|
164 |
+
# 4. Use EXACTLY this structure:
|
165 |
+
# # Data processing
|
166 |
+
# df_processed = df[...] # filtering/grouping
|
167 |
+
# # Save results
|
168 |
+
# df_processed.to_csv(f'{{output_dir}}/result.csv')
|
169 |
+
# # Visualizations (if needed)
|
170 |
+
# plt.figure()
|
171 |
+
# ... plotting code ...
|
172 |
+
# plt.savefig(f'{{output_dir}}/chart.png')
|
173 |
+
# plt.close()
|
174 |
+
|
175 |
+
# FORBIDDEN:
|
176 |
+
# - Function definitions
|
177 |
+
# - Dummy data creation
|
178 |
+
# - Any code blocks besides pandas operations and matplotlib
|
179 |
+
# - Print statements showing dataframes
|
180 |
+
|
181 |
+
# DATAFRAME COLUMNS: {', '.join(self.df.columns)}
|
182 |
+
# DATAFRAME'S FIRST FIVE ROWS: {self.df.head().to_dict('records')}
|
183 |
+
# USER QUERY: {query}
|
184 |
+
|
185 |
+
# EXAMPLE RESPONSE FOR "Sales by region":
|
186 |
+
# # Data processing
|
187 |
+
# sales_by_region = df.groupby('region')['sales'].sum().reset_index()
|
188 |
+
# # Save results
|
189 |
+
# sales_by_region.to_csv(f'{{output_dir}}/sales_by_region.csv')
|
190 |
+
# """
|
191 |
+
|
192 |
+
# def _generate_retry_prompt(self, query: str, error: str, code: str) -> str:
|
193 |
+
# return f"""FIX THIS CODE (failed with: {error}) by STRICTLY FOLLOWING:
|
194 |
|
195 |
+
# 1. REMOVE ALL FUNCTION DEFINITIONS
|
196 |
+
# 2. ENSURE DIRECT DF OPERATIONS
|
197 |
+
# 3. USE EXPLICIT output_dir PATHS
|
198 |
+
# 4. ADD NECESSARY IMPORTS IF MISSING
|
199 |
+
# 5. VALIDATE COLUMN NAMES EXIST
|
200 |
|
201 |
+
# BAD CODE:
|
202 |
+
# {code}
|
203 |
|
204 |
+
# CORRECTED CODE:"""
|
205 |
|
206 |
+
# def initialize_model(self, api_keys: List[str]) -> bool:
|
207 |
+
# self.key_manager = GeminiKeyManager(api_keys)
|
208 |
+
# if not self.key_manager.configure():
|
209 |
+
# raise RuntimeError("API key initialization failed")
|
210 |
+
# try:
|
211 |
+
# self.gemini_model = GenerativeModel(MODEL_NAME)
|
212 |
+
# return True
|
213 |
+
# except Exception as e:
|
214 |
+
# logger.error(f"Model init failed: {str(e)}")
|
215 |
+
# return False
|
216 |
|
217 |
+
# def generate_code(self, query: str, error: Optional[str] = None, previous_code: Optional[str] = None) -> str:
|
218 |
+
# prompt = self._generate_retry_prompt(query, error, previous_code) if error else self._generate_initial_prompt(query)
|
219 |
+
# try:
|
220 |
+
# response = self.gemini_model.generate_content(prompt)
|
221 |
+
# return self._extract_code(response.text)
|
222 |
+
# except Exception as e:
|
223 |
+
# if self.key_manager.available_keys and self.key_manager.configure():
|
224 |
+
# return self.generate_code(query, error, previous_code)
|
225 |
+
# raise
|
226 |
|
227 |
+
# def execute_query(self, query: str) -> Dict[str, Any]:
|
228 |
+
# self.repl = PythonREPL(self.df)
|
229 |
+
# result = None
|
230 |
|
231 |
+
# while self.current_retry < self.max_retries:
|
232 |
+
# try:
|
233 |
+
# code = self.generate_code(query,
|
234 |
+
# result["error_message"] if result else None,
|
235 |
+
# result["code"] if result else None)
|
236 |
+
# execution_result = self.repl.execute(code)
|
237 |
|
238 |
+
# if execution_result["error"]:
|
239 |
+
# self.current_retry += 1
|
240 |
+
# result = {
|
241 |
+
# "error_message": execution_result["error_message"],
|
242 |
+
# "code": code
|
243 |
+
# }
|
244 |
+
# else:
|
245 |
+
# return {
|
246 |
+
# "text": execution_result["output"],
|
247 |
+
# "csv_files": execution_result["files"]["csv"],
|
248 |
+
# "image_files": execution_result["files"]["images"]
|
249 |
+
# }
|
250 |
+
# except Exception as e:
|
251 |
+
# return {
|
252 |
+
# "error": f"Critical failure: {str(e)}",
|
253 |
+
# "csv_files": [],
|
254 |
+
# "image_files": []
|
255 |
+
# }
|
256 |
|
257 |
+
# return {
|
258 |
+
# "error": f"Failed after {self.max_retries} retries",
|
259 |
+
# "csv_files": [],
|
260 |
+
# "image_files": []
|
261 |
+
# }
|
262 |
+
|
263 |
+
# def gemini_llm_chat(csv_url: str, query: str) -> Dict[str, Any]:
|
264 |
+
# try:
|
265 |
+
# df = pd.read_csv(csv_url)
|
266 |
+
# agent = RethinkAgent(df=df)
|
267 |
|
268 |
+
# if not agent.initialize_model(API_KEYS):
|
269 |
+
# return {"error": "API configuration failed"}
|
270 |
|
271 |
+
# result = agent.execute_query(query)
|
272 |
|
273 |
+
# if "error" in result:
|
274 |
+
# return result
|
275 |
|
276 |
+
# return {
|
277 |
+
# "message": result["text"],
|
278 |
+
# "csv_files": result["csv_files"],
|
279 |
+
# "image_files": result["image_files"]
|
280 |
+
# }
|
281 |
+
# except Exception as e:
|
282 |
+
# logger.error(f"Processing failed: {str(e)}")
|
283 |
+
# return {
|
284 |
+
# "error": f"Processing error: {str(e)}",
|
285 |
+
# "csv_files": [],
|
286 |
+
# "image_files": []
|
287 |
+
# }
|
288 |
|
289 |
|
290 |
+
# async def generate_csv_report(csv_url: str, query: str) -> FileBoxProps:
|
291 |
+
# try:
|
292 |
+
# result = gemini_llm_chat(csv_url, query)
|
293 |
+
# logger.info(f"Raw result from gemini_llm_chat: {result}")
|
294 |
|
295 |
+
# csv_files = []
|
296 |
+
# image_files = []
|
297 |
|
298 |
+
# # Check if we got the expected response structure
|
299 |
+
# if isinstance(result, dict) and 'csv_files' in result and 'image_files' in result:
|
300 |
+
# # Process CSV files
|
301 |
+
# for csv_path in result['csv_files']:
|
302 |
+
# if os.path.exists(csv_path):
|
303 |
+
# file_name = os.path.basename(csv_path)
|
304 |
+
# try:
|
305 |
+
# unique_file_name = f"{uuid.uuid4()}_{file_name}"
|
306 |
+
# public_url = await upload_file_to_supabase(
|
307 |
+
# file_path=csv_path,
|
308 |
+
# file_name=unique_file_name
|
309 |
+
# )
|
310 |
+
# csv_files.append(FileProps(
|
311 |
+
# fileName=file_name,
|
312 |
+
# filePath=public_url,
|
313 |
+
# fileType="csv"
|
314 |
+
# ))
|
315 |
+
# os.remove(csv_path) # Clean up
|
316 |
+
# except Exception as upload_error:
|
317 |
+
# logger.error(f"Failed to upload CSV {file_name}: {str(upload_error)}")
|
318 |
+
# continue
|
319 |
+
|
320 |
+
# # Process image files
|
321 |
+
# for img_path in result['image_files']:
|
322 |
+
# if os.path.exists(img_path):
|
323 |
+
# file_name = os.path.basename(img_path)
|
324 |
+
# try:
|
325 |
+
# unique_file_name = f"{uuid.uuid4()}_{file_name}"
|
326 |
+
# public_url = await upload_file_to_supabase(
|
327 |
+
# file_path=img_path,
|
328 |
+
# file_name=unique_file_name
|
329 |
+
# )
|
330 |
+
# image_files.append(FileProps(
|
331 |
+
# fileName=file_name,
|
332 |
+
# filePath=public_url,
|
333 |
+
# fileType="image"
|
334 |
+
# ))
|
335 |
+
# os.remove(img_path) # Clean up
|
336 |
+
# except Exception as upload_error:
|
337 |
+
# logger.error(f"Failed to upload image {file_name}: {str(upload_error)}")
|
338 |
+
# continue
|
339 |
+
|
340 |
+
# return FileBoxProps(
|
341 |
+
# files=Files(
|
342 |
+
# csv_files=csv_files,
|
343 |
+
# image_files=image_files
|
344 |
+
# )
|
345 |
+
# )
|
346 |
+
# else:
|
347 |
+
# raise ValueError("Unexpected response format from gemini_llm_chat")
|
348 |
|
349 |
+
# except Exception as e:
|
350 |
+
# logger.error(f"Report generation failed: {str(e)}")
|
351 |
+
# # Return empty response but log the files we found
|
352 |
+
# if 'csv_files' in locals() and 'image_files' in locals():
|
353 |
+
# logger.info(f"Files that were generated but not processed: CSV: {result.get('csv_files', [])}, Images: {result.get('image_files', [])}")
|
354 |
+
# return FileBoxProps(
|
355 |
+
# files=Files(
|
356 |
+
# csv_files=[],
|
357 |
+
# image_files=[]
|
358 |
+
# )
|
359 |
+
# )
|
360 |
+
|
361 |
+
|
362 |
+
|
363 |
+
|
364 |
+
|
365 |
+
|
366 |
+
|
367 |
+
|
368 |
+
|
369 |
+
|
370 |
+
|
371 |
+
|
372 |
+
|
373 |
+
|
374 |
+
|
375 |
+
|
376 |
+
|
377 |
+
|
378 |
+
|
379 |
+
|
380 |
+
|
381 |
+
|
382 |
+
|
383 |
+
|
384 |
+
|
385 |
+
|
386 |
+
|
387 |
+
# Newly Modified code with openai
|
388 |
+
|
389 |
+
# Import necessary modules
|
390 |
+
import asyncio
|
391 |
+
import os
|
392 |
+
import threading
|
393 |
+
from typing import Any, Dict, Union
|
394 |
+
import uuid
|
395 |
+
from fastapi.encoders import jsonable_encoder
|
396 |
+
from langchain_openai import ChatOpenAI
|
397 |
+
import numpy as np
|
398 |
+
import pandas as pd
|
399 |
+
from pandasai import SmartDataframe
|
400 |
+
from langchain_groq.chat_models import ChatGroq
|
401 |
+
from dotenv import load_dotenv
|
402 |
+
from pydantic import BaseModel
|
403 |
+
from csv_service import clean_data, extract_chart_filenames
|
404 |
+
from langchain_groq import ChatGroq
|
405 |
+
import pandas as pd
|
406 |
+
from langchain_experimental.tools import PythonAstREPLTool
|
407 |
+
from langchain_experimental.agents import create_pandas_dataframe_agent
|
408 |
+
import numpy as np
|
409 |
+
import matplotlib.pyplot as plt
|
410 |
+
import matplotlib
|
411 |
+
import seaborn as sns
|
412 |
+
from gemini_langchain_agent import langchain_gemini_csv_handler
|
413 |
+
from supabase_service import upload_file_to_supabase
|
414 |
+
from util_service import _prompt_generator, process_answer
|
415 |
+
import matplotlib
|
416 |
+
import logging
|
417 |
+
matplotlib.use('Agg')
|
418 |
+
|
419 |
+
|
420 |
+
load_dotenv()
|
421 |
+
|
422 |
+
image_file_path = os.getenv("IMAGE_FILE_PATH")
|
423 |
+
image_not_found = os.getenv("IMAGE_NOT_FOUND")
|
424 |
+
allowed_hosts = os.getenv("ALLOWED_HOSTS", "").split(",")
|
425 |
+
|
426 |
+
|
427 |
+
# Load environment variables
|
428 |
+
groq_api_keys = os.getenv("GROQ_API_KEYS").split(",")
|
429 |
+
model_name = os.getenv("GROQ_LLM_MODEL")
|
430 |
+
|
431 |
+
openai_api_keys = os.getenv("OPENAI_API_KEYS").split(",")
|
432 |
+
openai_base_url = os.getenv("OPENAI_BASE_URL")
|
433 |
+
openai_api_base = os.getenv("OPENAI_BASE_URL")
|
434 |
+
|
435 |
+
# Set up logging
|
436 |
+
logging.basicConfig(level=logging.INFO)
|
437 |
+
logger = logging.getLogger(__name__)
|
438 |
+
|
439 |
+
class CsvUrlRequest(BaseModel):
|
440 |
+
csv_url: str
|
441 |
+
|
442 |
+
class ImageRequest(BaseModel):
|
443 |
+
image_path: str
|
444 |
+
|
445 |
+
class CsvCommonHeadersRequest(BaseModel):
|
446 |
+
file_urls: list[str]
|
447 |
+
|
448 |
+
class CsvsMergeRequest(BaseModel):
|
449 |
+
file_urls: list[str]
|
450 |
+
merge_type: str
|
451 |
+
common_columns_name: list[str]
|
452 |
+
|
453 |
+
# Thread-safe key management for openai_chat
|
454 |
+
current_openai_key_index = 0
|
455 |
+
current_openai_key_lock = threading.Lock()
|
456 |
+
|
457 |
+
# Thread-safe key management for groq_chat
|
458 |
+
current_groq_key_index = 0
|
459 |
+
current_groq_key_lock = threading.Lock()
|
460 |
+
|
461 |
+
# Thread-safe key management for langchain_csv_chat
|
462 |
+
current_langchain_key_index = 0
|
463 |
+
current_langchain_key_lock = threading.Lock()
|
464 |
+
|
465 |
+
|
466 |
+
# CHAT CODING STARTS FROM HERE
|
467 |
+
def handle_out_of_range_float(value):
|
468 |
+
if isinstance(value, float):
|
469 |
+
if np.isnan(value):
|
470 |
+
return None
|
471 |
+
elif np.isinf(value):
|
472 |
+
return "Infinity"
|
473 |
+
return value
|
474 |
+
|
475 |
+
|
476 |
+
# Modified groq_chat function with thread-safe key rotation
|
477 |
+
def groq_chat(csv_url: str, question: str):
|
478 |
+
global current_groq_key_index, current_groq_key_lock
|
479 |
+
|
480 |
+
while True:
|
481 |
+
with current_groq_key_lock:
|
482 |
+
if current_groq_key_index >= len(groq_api_keys):
|
483 |
+
return {"error": "All API keys exhausted."}
|
484 |
+
current_api_key = groq_api_keys[current_groq_key_index]
|
485 |
+
|
486 |
+
try:
|
487 |
+
# Delete cache file if exists
|
488 |
+
cache_db_path = "/workspace/cache/cache_db_0.11.db"
|
489 |
+
if os.path.exists(cache_db_path):
|
490 |
+
try:
|
491 |
+
os.remove(cache_db_path)
|
492 |
+
except Exception as e:
|
493 |
+
print(f"Error deleting cache DB file: {e}")
|
494 |
+
|
495 |
+
data = clean_data(csv_url)
|
496 |
+
llm = ChatGroq(model=model_name, api_key=current_api_key)
|
497 |
+
# Generate unique filename using UUID
|
498 |
+
chart_filename = f"chart_{uuid.uuid4()}.png"
|
499 |
+
chart_path = os.path.join("generated_charts", chart_filename)
|
500 |
+
|
501 |
+
# Configure SmartDataframe with chart settings
|
502 |
+
df = SmartDataframe(
|
503 |
+
data,
|
504 |
+
config={
|
505 |
+
'llm': llm,
|
506 |
+
'save_charts': True, # Enable chart saving
|
507 |
+
'open_charts': False,
|
508 |
+
'save_charts_path': os.path.dirname(chart_path), # Directory to save
|
509 |
+
'custom_chart_filename': chart_filename # Unique filename
|
510 |
+
}
|
511 |
+
)
|
512 |
+
|
513 |
+
answer = df.chat(question)
|
514 |
+
|
515 |
+
# Process different response types
|
516 |
+
if isinstance(answer, pd.DataFrame):
|
517 |
+
processed = answer.apply(handle_out_of_range_float).to_dict(orient="records")
|
518 |
+
elif isinstance(answer, pd.Series):
|
519 |
+
processed = answer.apply(handle_out_of_range_float).to_dict()
|
520 |
+
elif isinstance(answer, list):
|
521 |
+
processed = [handle_out_of_range_float(item) for item in answer]
|
522 |
+
elif isinstance(answer, dict):
|
523 |
+
processed = {k: handle_out_of_range_float(v) for k, v in answer.items()}
|
524 |
+
else:
|
525 |
+
processed = {"answer": str(handle_out_of_range_float(answer))}
|
526 |
+
|
527 |
+
return processed
|
528 |
+
|
529 |
+
except Exception as e:
|
530 |
+
error_message = str(e)
|
531 |
+
if error_message:
|
532 |
+
with current_groq_key_lock:
|
533 |
+
current_groq_key_index += 1
|
534 |
+
if current_groq_key_index >= len(groq_api_keys):
|
535 |
+
print("All API keys exhausted.")
|
536 |
+
return None
|
537 |
+
else:
|
538 |
+
print(f"Error with API key index {current_groq_key_index}: {error_message}")
|
539 |
+
return None
|
540 |
+
|
541 |
+
|
542 |
+
|
543 |
+
|
544 |
+
|
545 |
+
|
546 |
+
|
547 |
+
# Modified langchain_csv_chat with thread-safe key rotation
|
548 |
+
def langchain_csv_chat(csv_url: str, question: str, chart_required: bool):
|
549 |
+
global current_langchain_key_index, current_langchain_key_lock
|
550 |
+
|
551 |
+
data = clean_data(csv_url)
|
552 |
+
attempts = 0
|
553 |
+
|
554 |
+
while attempts < len(groq_api_keys):
|
555 |
+
with current_langchain_key_lock:
|
556 |
+
if current_langchain_key_index >= len(groq_api_keys):
|
557 |
+
current_langchain_key_index = 0
|
558 |
+
api_key = groq_api_keys[current_langchain_key_index]
|
559 |
+
current_key = current_langchain_key_index
|
560 |
+
current_langchain_key_index += 1
|
561 |
+
attempts += 1
|
562 |
+
|
563 |
+
try:
|
564 |
+
llm = ChatGroq(model=model_name, api_key=api_key)
|
565 |
+
tool = PythonAstREPLTool(locals={
|
566 |
+
"df": data,
|
567 |
+
"pd": pd,
|
568 |
+
"np": np,
|
569 |
+
"plt": plt,
|
570 |
+
"sns": sns,
|
571 |
+
"matplotlib": matplotlib
|
572 |
+
})
|
573 |
+
|
574 |
+
agent = create_pandas_dataframe_agent(
|
575 |
+
llm,
|
576 |
+
data,
|
577 |
+
agent_type="tool-calling",
|
578 |
+
verbose=True,
|
579 |
+
allow_dangerous_code=True,
|
580 |
+
extra_tools=[tool],
|
581 |
+
return_intermediate_steps=True
|
582 |
+
)
|
583 |
+
|
584 |
+
prompt = _prompt_generator(question, chart_required)
|
585 |
+
result = agent.invoke({"input": prompt})
|
586 |
+
return result.get("output")
|
587 |
+
|
588 |
+
except Exception as e:
|
589 |
+
print(f"Error with key index {current_key}: {str(e)}")
|
590 |
+
|
591 |
+
# If all keys are exhausted, return None
|
592 |
+
print("All API keys have been exhausted.")
|
593 |
+
return None
|
594 |
+
|
595 |
+
|
596 |
+
def handle_out_of_range_float(value):
|
597 |
+
if isinstance(value, float):
|
598 |
+
if np.isnan(value):
|
599 |
+
return None
|
600 |
+
elif np.isinf(value):
|
601 |
+
return "Infinity"
|
602 |
+
return value
|
603 |
+
|
604 |
+
|
605 |
+
|
606 |
+
|
607 |
+
|
608 |
+
|
609 |
+
|
610 |
+
# CHART CODING STARTS FROM HERE
|
611 |
+
|
612 |
+
instructions = """
|
613 |
+
|
614 |
+
- Please ensure that each value is clearly visible, You may need to adjust the font size, rotate the labels, or use truncation to improve readability (if needed).
|
615 |
+
- For multiple charts, arrange them in a grid format (2x2, 3x3, etc.)
|
616 |
+
- Use colorblind-friendly palette
|
617 |
+
- Read above instructions and follow them.
|
618 |
+
|
619 |
+
"""
|
620 |
+
|
621 |
+
# Thread-safe configuration for chart endpoints
|
622 |
+
current_groq_chart_key_index = 0
|
623 |
+
current_groq_chart_lock = threading.Lock()
|
624 |
+
|
625 |
+
current_langchain_chart_key_index = 0
|
626 |
+
current_langchain_chart_lock = threading.Lock()
|
627 |
+
|
628 |
+
def model():
|
629 |
+
global current_groq_chart_key_index, current_groq_chart_lock
|
630 |
+
with current_groq_chart_lock:
|
631 |
+
if current_groq_chart_key_index >= len(groq_api_keys):
|
632 |
+
raise Exception("All API keys exhausted for chart generation")
|
633 |
+
api_key = groq_api_keys[current_groq_chart_key_index]
|
634 |
+
return ChatGroq(model=model_name, api_key=api_key)
|
635 |
+
|
636 |
+
def groq_chart(csv_url: str, question: str):
|
637 |
+
global current_groq_chart_key_index, current_groq_chart_lock
|
638 |
+
|
639 |
+
for attempt in range(len(groq_api_keys)):
|
640 |
+
try:
|
641 |
+
# Clean cache before processing
|
642 |
+
cache_db_path = "/workspace/cache/cache_db_0.11.db"
|
643 |
+
if os.path.exists(cache_db_path):
|
644 |
+
try:
|
645 |
+
os.remove(cache_db_path)
|
646 |
+
except Exception as e:
|
647 |
+
print(f"Cache cleanup error: {e}")
|
648 |
+
|
649 |
+
data = clean_data(csv_url)
|
650 |
+
with current_groq_chart_lock:
|
651 |
+
current_api_key = groq_api_keys[current_groq_chart_key_index]
|
652 |
+
|
653 |
+
llm = ChatGroq(model=model_name, api_key=current_api_key)
|
654 |
+
|
655 |
+
# Generate unique filename using UUID
|
656 |
+
chart_filename = f"chart_{uuid.uuid4()}.png"
|
657 |
+
chart_path = os.path.join("generated_charts", chart_filename)
|
658 |
+
|
659 |
+
# Configure SmartDataframe with chart settings
|
660 |
+
df = SmartDataframe(
|
661 |
+
data,
|
662 |
+
config={
|
663 |
+
'llm': llm,
|
664 |
+
'save_charts': True, # Enable chart saving
|
665 |
+
'open_charts': False,
|
666 |
+
'save_charts_path': os.path.dirname(chart_path), # Directory to save
|
667 |
+
'custom_chart_filename': chart_filename # Unique filename
|
668 |
+
}
|
669 |
+
)
|
670 |
+
|
671 |
+
answer = df.chat(question + instructions)
|
672 |
+
|
673 |
+
if process_answer(answer):
|
674 |
+
return "Chart not generated"
|
675 |
+
return answer
|
676 |
+
|
677 |
+
except Exception as e:
|
678 |
+
error = str(e)
|
679 |
+
if "429" in error or error is not None:
|
680 |
+
with current_groq_chart_lock:
|
681 |
+
current_groq_chart_key_index = (current_groq_chart_key_index + 1) % len(groq_api_keys)
|
682 |
+
else:
|
683 |
+
print(f"Chart generation error: {error}")
|
684 |
+
return {"error": error}
|
685 |
+
|
686 |
+
print("All API keys exhausted for chart generation")
|
687 |
+
return None
|
688 |
+
|
689 |
+
|
690 |
+
|
691 |
+
def langchain_csv_chart(csv_url: str, question: str, chart_required: bool):
|
692 |
+
global current_langchain_chart_key_index, current_langchain_chart_lock
|
693 |
+
|
694 |
+
data = clean_data(csv_url)
|
695 |
+
|
696 |
+
for attempt in range(len(groq_api_keys)):
|
697 |
+
try:
|
698 |
+
with current_langchain_chart_lock:
|
699 |
+
api_key = groq_api_keys[current_langchain_chart_key_index]
|
700 |
+
current_key = current_langchain_chart_key_index
|
701 |
+
current_langchain_chart_key_index = (current_langchain_chart_key_index + 1) % len(groq_api_keys)
|
702 |
+
|
703 |
+
llm = ChatGroq(model=model_name, api_key=api_key)
|
704 |
+
tool = PythonAstREPLTool(locals={
|
705 |
+
"df": data,
|
706 |
+
"pd": pd,
|
707 |
+
"np": np,
|
708 |
+
"plt": plt,
|
709 |
+
"sns": sns,
|
710 |
+
"matplotlib": matplotlib,
|
711 |
+
"uuid": uuid
|
712 |
+
})
|
713 |
+
|
714 |
+
agent = create_pandas_dataframe_agent(
|
715 |
+
llm,
|
716 |
+
data,
|
717 |
+
agent_type="tool-calling",
|
718 |
+
verbose=True,
|
719 |
+
allow_dangerous_code=True,
|
720 |
+
extra_tools=[tool],
|
721 |
+
return_intermediate_steps=True
|
722 |
+
)
|
723 |
+
|
724 |
+
result = agent.invoke({"input": _prompt_generator(f"{question} and use this csv_url: {csv_url} to read the csv file", True)})
|
725 |
+
output = result.get("output", "")
|
726 |
+
|
727 |
+
# Verify chart file creation
|
728 |
+
chart_files = extract_chart_filenames(output)
|
729 |
+
if len(chart_files) > 0:
|
730 |
+
return chart_files
|
731 |
+
|
732 |
+
if attempt < len(groq_api_keys) - 1:
|
733 |
+
print(f"Langchain chart error (key {current_key}): {output}")
|
734 |
+
|
735 |
+
except Exception as e:
|
736 |
+
print(f"Langchain chart error (key {current_key}): {str(e)}")
|
737 |
+
|
738 |
+
print("All API keys exhausted for chart generation")
|
739 |
+
return None
|
740 |
+
|
741 |
+
|
742 |
+
####################################### OpenAI + PandasAI #######################################
|
743 |
+
|
744 |
+
|
745 |
+
|
746 |
+
|
747 |
+
# Modified openai_chat function with thread-safe key rotation
|
748 |
+
openai_model_name = 'gpt-4o'
|
749 |
+
|
750 |
+
def openai_chat(csv_url: str, question: str):
|
751 |
+
global current_openai_key_index, current_openai_key_lock
|
752 |
+
|
753 |
+
while True:
|
754 |
+
with current_openai_key_lock:
|
755 |
+
if current_openai_key_index >= len(openai_api_keys):
|
756 |
+
return {"error": "All API keys exhausted."}
|
757 |
+
current_api_key = openai_api_keys[current_openai_key_index]
|
758 |
+
|
759 |
+
try:
|
760 |
+
# Delete cache file if exists
|
761 |
+
cache_db_path = "/workspace/cache/cache_db_0.11.db"
|
762 |
+
if os.path.exists(cache_db_path):
|
763 |
+
try:
|
764 |
+
os.remove(cache_db_path)
|
765 |
+
except Exception as e:
|
766 |
+
print(f"Error deleting cache DB file: {e}")
|
767 |
+
|
768 |
+
data = clean_data(csv_url)
|
769 |
+
llm = ChatOpenAI(model=openai_model_name, api_key=current_api_key,base_url=openai_api_base)
|
770 |
+
# Generate unique filename using UUID
|
771 |
+
chart_filename = f"chart_{uuid.uuid4()}.png"
|
772 |
+
chart_path = os.path.join("generated_charts", chart_filename)
|
773 |
|
774 |
+
# Configure SmartDataframe with chart settings
|
775 |
+
df = SmartDataframe(
|
776 |
+
data,
|
777 |
+
config={
|
778 |
+
'llm': llm,
|
779 |
+
'save_charts': True, # Enable chart saving
|
780 |
+
'open_charts': False,
|
781 |
+
'save_charts_path': os.path.dirname(chart_path), # Directory to save
|
782 |
+
'custom_chart_filename': chart_filename # Unique filename
|
783 |
+
}
|
784 |
)
|
|
|
|
|
785 |
|
786 |
+
answer = df.chat(question)
|
787 |
+
# Process different response types
|
788 |
+
if isinstance(answer, pd.DataFrame):
|
789 |
+
processed = answer.apply(handle_out_of_range_float).to_dict(orient="records")
|
790 |
+
elif isinstance(answer, pd.Series):
|
791 |
+
processed = answer.apply(handle_out_of_range_float).to_dict()
|
792 |
+
elif isinstance(answer, list):
|
793 |
+
processed = [handle_out_of_range_float(item) for item in answer]
|
794 |
+
elif isinstance(answer, dict):
|
795 |
+
processed = {k: handle_out_of_range_float(v) for k, v in answer.items()}
|
796 |
+
else:
|
797 |
+
processed = {"answer": str(handle_out_of_range_float(answer))}
|
798 |
+
|
799 |
+
return processed
|
800 |
+
|
801 |
+
except Exception as e:
|
802 |
+
error_message = str(e)
|
803 |
+
if error_message:
|
804 |
+
with current_openai_key_lock:
|
805 |
+
current_openai_key_index += 1
|
806 |
+
if current_openai_key_index >= len(openai_api_keys):
|
807 |
+
print("All API keys exhausted.")
|
808 |
+
return None
|
809 |
+
else:
|
810 |
+
print(f"Error with API key index {current_openai_key_index}: {error_message}")
|
811 |
+
return None
|
812 |
+
|
813 |
+
|
814 |
+
|
815 |
+
|
816 |
+
|
817 |
+
|
818 |
+
def openai_chart(csv_url: str, question: str):
|
819 |
+
global current_openai_key_index, current_openai_key_lock
|
820 |
+
|
821 |
+
while True:
|
822 |
+
with current_openai_key_lock:
|
823 |
+
if current_openai_key_index >= len(openai_api_keys):
|
824 |
+
return {"error": "All API keys exhausted."}
|
825 |
+
current_api_key = openai_api_keys[current_openai_key_index]
|
826 |
+
|
827 |
+
try:
|
828 |
+
# Delete cache file if exists
|
829 |
+
cache_db_path = "/workspace/cache/cache_db_0.11.db"
|
830 |
+
if os.path.exists(cache_db_path):
|
831 |
+
try:
|
832 |
+
os.remove(cache_db_path)
|
833 |
+
except Exception as e:
|
834 |
+
print(f"Error deleting cache DB file: {e}")
|
835 |
+
|
836 |
+
data = clean_data(csv_url)
|
837 |
+
llm = ChatOpenAI(model=openai_model_name, api_key=current_api_key,base_url=openai_api_base)
|
838 |
+
# Generate unique filename using UUID
|
839 |
+
chart_filename = f"chart_{uuid.uuid4()}.png"
|
840 |
+
chart_path = os.path.join("generated_charts", chart_filename)
|
841 |
+
|
842 |
+
# Configure SmartDataframe with chart settings
|
843 |
+
df = SmartDataframe(
|
844 |
+
data,
|
845 |
+
config={
|
846 |
+
'llm': llm,
|
847 |
+
'save_charts': True, # Enable chart saving
|
848 |
+
'open_charts': False,
|
849 |
+
'save_charts_path': os.path.dirname(chart_path), # Directory to save
|
850 |
+
'custom_chart_filename': chart_filename # Unique filename
|
851 |
+
}
|
852 |
)
|
853 |
+
|
854 |
+
answer = df.chat(question + instructions)
|
855 |
+
|
856 |
+
if process_answer(answer):
|
857 |
+
return "Chart not generated"
|
858 |
+
return answer
|
859 |
+
|
860 |
+
except Exception as e:
|
861 |
+
error = str(e)
|
862 |
+
print(f"Error with API key index {current_openai_key_index}: {error}")
|
863 |
+
if "429" in error or error is not None:
|
864 |
+
with current_openai_key_lock:
|
865 |
+
current_openai_key_index = (current_openai_key_index + 1) % len(openai_api_keys)
|
866 |
+
else:
|
867 |
+
print(f"Chart generation error: {error}")
|
868 |
+
return {"error": error}
|
869 |
+
|
870 |
+
print("All API keys exhausted for chart generation")
|
871 |
+
return None
|
872 |
+
|
873 |
+
|
874 |
+
|
875 |
+
|
876 |
+
|
877 |
+
####################################### Start with lc_gemini #######################################
|
878 |
+
|
879 |
+
|
880 |
+
# async def csv_chat(csv_url: str, query: str):
|
881 |
+
# """
|
882 |
+
# Generate a response based on the provided CSV URL and query.
|
883 |
+
# Prioritizes LangChain-Gemini, then LangChain-Groq, then raw OpenAI and finally raw Groq as fallback.
|
884 |
+
|
885 |
+
# Parameters:
|
886 |
+
# - csv_url (str): The URL of the CSV file.
|
887 |
+
# - query (str): The query for generating the response.
|
888 |
+
|
889 |
+
# Returns:
|
890 |
+
# - dict: A dictionary containing the generated response.
|
891 |
+
|
892 |
+
# Example:
|
893 |
+
# - csv_url: "https://example.com/data.csv"
|
894 |
+
# - query: "What is the total sales for the year 2022?"
|
895 |
+
# Returns:
|
896 |
+
# - dict: {"answer": "The total sales for 2022 is $100,000."}
|
897 |
+
# """
|
898 |
+
# try:
|
899 |
+
# updated_query = f"{query} and Do not show any charts or graphs."
|
900 |
+
|
901 |
+
# # --- 1. First Attempt: LangChain Gemini ---
|
902 |
+
# try:
|
903 |
+
# gemini_answer = await asyncio.to_thread(
|
904 |
+
# langchain_gemini_csv_handler, csv_url, updated_query, False
|
905 |
+
# )
|
906 |
+
# print("LangChain-Gemini answer:", gemini_answer)
|
907 |
+
|
908 |
+
# if not process_answer(gemini_answer) or gemini_answer is None:
|
909 |
+
# return {"answer": jsonable_encoder(gemini_answer)}
|
910 |
+
|
911 |
+
# raise Exception("LangChain-Gemini response not usable, falling back to LangChain-Groq")
|
912 |
+
|
913 |
+
# except Exception as gemini_error:
|
914 |
+
# print(f"LangChain-Gemini error: {str(gemini_error)}")
|
915 |
+
|
916 |
+
# # --- 2. Second Attempt: LangChain Groq ---
|
917 |
+
# try:
|
918 |
+
# lang_groq_answer = await asyncio.to_thread(
|
919 |
+
# langchain_csv_chat, csv_url, updated_query, False
|
920 |
+
# )
|
921 |
+
# print("LangChain-Groq answer:", lang_groq_answer)
|
922 |
+
|
923 |
+
# if not process_answer(lang_groq_answer):
|
924 |
+
# return {"answer": jsonable_encoder(lang_groq_answer)}
|
925 |
+
|
926 |
+
# raise Exception("LangChain-Groq response not usable, falling back to raw Groq")
|
927 |
+
|
928 |
+
# except Exception as lang_groq_error:
|
929 |
+
# print(f"LangChain-Groq error: {str(lang_groq_error)}")
|
930 |
+
|
931 |
+
# # --- 3. Final Attempt: Raw OpenAI Chat ---
|
932 |
+
# try:
|
933 |
+
# raw_openai_answer = await asyncio.to_thread(openai_chat, csv_url, updated_query)
|
934 |
+
# print("Raw OpenAI answer:", raw_openai_answer)
|
935 |
+
|
936 |
+
# if process_answer(raw_openai_answer) == "Empty response received." or raw_openai_answer is None:
|
937 |
+
# return {"answer": "Sorry, I couldn't find relevant data..."}
|
938 |
+
|
939 |
+
# if process_answer(raw_openai_answer):
|
940 |
+
# except Exception as openai_exception:
|
941 |
+
# print(f"Raw OpenAI error: {str(openai_exception)}")
|
942 |
+
|
943 |
+
|
944 |
+
# # --- 4. Final Attempt: Raw Groq Chat ---
|
945 |
+
# try:
|
946 |
+
# raw_groq_answer = await asyncio.to_thread(groq_chat, csv_url, updated_query)
|
947 |
+
# print("Raw Groq answer:", raw_groq_answer)
|
948 |
+
|
949 |
+
# if process_answer(raw_groq_answer) == "Empty response received." or raw_groq_answer is None:
|
950 |
+
# return {"answer": "Sorry, I couldn't find relevant data..."}
|
951 |
+
|
952 |
+
# if process_answer(raw_groq_answer):
|
953 |
+
# raise Exception("All fallbacks exhausted")
|
954 |
+
|
955 |
+
# return {"answer": jsonable_encoder(raw_groq_answer)}
|
956 |
+
|
957 |
+
# except Exception as raw_groq_error:
|
958 |
+
# print(f"Raw Groq error: {str(raw_groq_error)}")
|
959 |
+
# return {"answer": "error"}
|
960 |
+
|
961 |
+
# except Exception as e:
|
962 |
+
# print(f"Unexpected error: {str(e)}")
|
963 |
+
# return {"answer": "error"}
|
964 |
+
|
965 |
+
|
966 |
+
|
967 |
+
|
968 |
+
|
969 |
+
|
970 |
+
|
971 |
+
|
972 |
+
# async def csv_chart(csv_url: str, query: str):
|
973 |
+
# """
|
974 |
+
# Generate a chart based on the provided CSV URL and query.
|
975 |
+
# Prioritizes raw OpenAI, then raw Groq, then LangChain Gemini, and finally LangChain Groq as fallback.
|
976 |
+
|
977 |
+
# Parameters:
|
978 |
+
# - csv_url (str): The URL of the CSV file.
|
979 |
+
# - query (str): The query for generating the chart.
|
980 |
+
|
981 |
+
# Returns:
|
982 |
+
# - dict: A dictionary containing either:
|
983 |
+
# - {"image_url": "https://example.com/chart.png"} on success, or
|
984 |
+
# - {"error": "error message"} on failure
|
985 |
+
|
986 |
+
# Example:
|
987 |
+
# - csv_url: "https://example.com/data.csv"
|
988 |
+
# - query: "Show sales trends as a line chart"
|
989 |
+
# Returns:
|
990 |
+
# - dict: {"image_url": "https://storage.example.com/chart_uuid.png"}
|
991 |
+
# """
|
992 |
+
|
993 |
+
# async def upload_and_return(image_path: str) -> dict:
|
994 |
+
# """Helper function to handle image uploads"""
|
995 |
+
# unique_name = f'{uuid.uuid4()}.png'
|
996 |
+
# public_url = await upload_file_to_supabase(image_path, unique_name)
|
997 |
+
# print(f"Uploaded chart: {public_url}")
|
998 |
+
# os.remove(image_path) # Remove the local image file after upload
|
999 |
+
# return {"image_url": public_url}
|
1000 |
+
|
1001 |
+
# try:
|
1002 |
+
# # --- 1. First Attempt: Raw OpenAI ---
|
1003 |
+
# try:
|
1004 |
+
# openai_result = await asyncio.to_thread(openai_chart, csv_url, query)
|
1005 |
+
# print(f"OpenAI chart result:", openai_result)
|
1006 |
+
|
1007 |
+
# if openai_result and openai_result != 'Chart not generated':
|
1008 |
+
# return await upload_and_return(openai_result)
|
1009 |
+
|
1010 |
+
# raise Exception("OpenAI failed to generate chart")
|
1011 |
+
|
1012 |
+
# except Exception as openai_error:
|
1013 |
+
# print(f"OpenAI failed ({str(openai_error)}), trying LangChain Gemini...")
|
1014 |
+
|
1015 |
+
# # --- 2.. First Attempt: Raw Groq ---
|
1016 |
+
# try:
|
1017 |
+
# groq_result = await asyncio.to_thread(groq_chart, csv_url, query)
|
1018 |
+
# print(f"Raw Groq chart result:", groq_result)
|
1019 |
+
|
1020 |
+
# if groq_result and groq_result != 'Chart not generated':
|
1021 |
+
# return await upload_and_return(groq_result)
|
1022 |
+
|
1023 |
+
# raise Exception("Raw Groq failed to generate chart")
|
1024 |
+
|
1025 |
+
# except Exception as groq_error:
|
1026 |
+
# print(f"Raw Groq failed ({str(groq_error)}), trying LangChain Gemini...")
|
1027 |
+
|
1028 |
+
# # --- 3. Second Attempt: LangChain Gemini ---
|
1029 |
+
# try:
|
1030 |
+
# gemini_result = await asyncio.to_thread(
|
1031 |
+
# langchain_gemini_csv_handler, csv_url, query, True
|
1032 |
+
# )
|
1033 |
+
# print("LangChain Gemini chart result:", gemini_result)
|
1034 |
+
|
1035 |
+
# # --- i) If Gemini result is a string, return it ---
|
1036 |
+
# if gemini_result and isinstance(gemini_result, str):
|
1037 |
+
# clean_path = gemini_result.strip()
|
1038 |
+
# return await upload_and_return(clean_path)
|
1039 |
+
|
1040 |
+
# # --- ii) If Gemini result is a list, return the first element ---
|
1041 |
+
# if gemini_result and isinstance(gemini_result, list) and len(gemini_result) > 0:
|
1042 |
+
# return await upload_and_return(gemini_result[0])
|
1043 |
+
|
1044 |
+
# raise Exception("LangChain Gemini returned empty result")
|
1045 |
+
|
1046 |
+
# except Exception as gemini_error:
|
1047 |
+
# print(f"LangChain Gemini failed ({str(gemini_error)}), trying LangChain Groq...")
|
1048 |
+
|
1049 |
+
# # --- 4. Final Attempt: LangChain Groq ---
|
1050 |
+
# try:
|
1051 |
+
# lc_groq_paths = await asyncio.to_thread(
|
1052 |
+
# langchain_csv_chart, csv_url, query, True
|
1053 |
+
# )
|
1054 |
+
# print("LangChain Groq chart result:", lc_groq_paths)
|
1055 |
+
|
1056 |
+
# if isinstance(lc_groq_paths, list) and lc_groq_paths:
|
1057 |
+
# return await upload_and_return(lc_groq_paths[0])
|
1058 |
+
|
1059 |
+
# return {"error": "All chart generation methods failed"}
|
1060 |
+
|
1061 |
+
# except Exception as lc_groq_error:
|
1062 |
+
# print(f"LangChain Groq failed: {str(lc_groq_error)}")
|
1063 |
+
# return {"error": "Could not generate chart"}
|
1064 |
+
|
1065 |
+
# except Exception as e:
|
1066 |
+
# print(f"Critical error: {str(e)}")
|
1067 |
+
# return {"error": "Internal system error"}
|
1068 |
+
|
1069 |
+
|
1070 |
+
|
1071 |
+
####################################### Optimized Version #######################################
|
1072 |
+
|
1073 |
+
|
1074 |
+
async def csv_chat(csv_url: str, query: str) -> Dict[str, Any]:
|
1075 |
+
"""
|
1076 |
+
Generate a response based on the provided CSV URL and query.
|
1077 |
+
Prioritizes LangChain-Gemini, then LangChain-Groq, then raw OpenAI and finally raw Groq as fallback.
|
1078 |
+
|
1079 |
+
Parameters:
|
1080 |
+
- csv_url (str): The URL of the CSV file.
|
1081 |
+
- query (str): The query for generating the response.
|
1082 |
+
|
1083 |
+
Returns:
|
1084 |
+
- dict: A dictionary containing the generated response or error message.
|
1085 |
+
|
1086 |
+
Example:
|
1087 |
+
- csv_url: "https://example.com/data.csv"
|
1088 |
+
- query: "What is the total sales for the year 2022?"
|
1089 |
+
Returns:
|
1090 |
+
- dict: {"answer": "The total sales for 2022 is $100,000."}
|
1091 |
+
"""
|
1092 |
+
updated_query = f"{query} and Do not show any charts or graphs."
|
1093 |
+
fallback_answer = "Sorry, I couldn't find relevant data..."
|
1094 |
+
error_answer = "An error occurred while processing your request."
|
1095 |
+
|
1096 |
+
async def try_chat_method(method_name: str, method, *args) -> Dict[str, Any]:
|
1097 |
+
"""Attempt to get answer from a specific chat method"""
|
1098 |
+
try:
|
1099 |
+
logger.info(f"Attempting {method_name}")
|
1100 |
+
answer = await asyncio.to_thread(method, *args)
|
1101 |
+
|
1102 |
+
if answer is None:
|
1103 |
+
logger.warning(f"{method_name} returned None")
|
1104 |
+
return {"status": "empty", "answer": None}
|
1105 |
+
|
1106 |
+
processed = process_answer(answer)
|
1107 |
+
if processed == "Empty response received.":
|
1108 |
+
logger.warning(f"{method_name} returned empty response")
|
1109 |
+
return {"status": "empty", "answer": answer}
|
1110 |
+
elif processed:
|
1111 |
+
logger.warning(f"{method_name} response not usable")
|
1112 |
+
return {"status": "invalid", "answer": answer}
|
1113 |
+
else:
|
1114 |
+
logger.info(f"{method_name} succeeded")
|
1115 |
+
return {"status": "success", "answer": answer}
|
1116 |
+
|
1117 |
+
except Exception as e:
|
1118 |
+
logger.error(f"{method_name} failed: {str(e)}")
|
1119 |
+
return {"status": "error", "error": str(e)}
|
1120 |
+
|
1121 |
+
# Define the methods to try in priority order
|
1122 |
+
chat_methods = [
|
1123 |
+
("LangChain-Gemini", langchain_gemini_csv_handler, csv_url, updated_query, False),
|
1124 |
+
("LangChain-Groq", langchain_csv_chat, csv_url, updated_query, False),
|
1125 |
+
("Raw OpenAI", openai_chat, csv_url, updated_query),
|
1126 |
+
("Raw Groq", groq_chat, csv_url, updated_query),
|
1127 |
+
]
|
1128 |
+
|
1129 |
+
for method_name, method, *args in chat_methods:
|
1130 |
+
result = await try_chat_method(method_name, method, *args)
|
1131 |
+
|
1132 |
+
if result["status"] == "success":
|
1133 |
+
return {"answer": jsonable_encoder(result["answer"])}
|
1134 |
+
elif result["status"] == "empty":
|
1135 |
+
return {"answer": fallback_answer}
|
1136 |
+
|
1137 |
+
# If all methods failed or returned invalid responses
|
1138 |
+
logger.error("All chat methods failed to produce a valid response")
|
1139 |
+
return {"answer": error_answer}
|
1140 |
+
|
1141 |
+
|
1142 |
+
|
1143 |
+
|
1144 |
+
|
1145 |
+
|
1146 |
+
|
1147 |
+
|
1148 |
+
|
1149 |
+
async def csv_chart(csv_url: str, query: str) -> Dict[str, str]:
|
1150 |
+
"""
|
1151 |
+
Generate a chart based on the provided CSV URL and query.
|
1152 |
+
Prioritizes raw OpenAI, then raw Groq, then LangChain Gemini, and finally LangChain Groq as fallback.
|
1153 |
+
|
1154 |
+
Parameters:
|
1155 |
+
- csv_url (str): The URL of the CSV file.
|
1156 |
+
- query (str): The query for generating the chart.
|
1157 |
+
|
1158 |
+
Returns:
|
1159 |
+
- dict: A dictionary containing either:
|
1160 |
+
- {"image_url": "https://example.com/chart.png"} on success, or
|
1161 |
+
- {"error": "error message"} on failure
|
1162 |
+
|
1163 |
+
Example:
|
1164 |
+
- csv_url: "https://example.com/data.csv"
|
1165 |
+
- query: "Show sales trends as a line chart"
|
1166 |
+
Returns:
|
1167 |
+
- dict: {"image_url": "https://storage.example.com/chart_uuid.png"}
|
1168 |
+
"""
|
1169 |
+
async def upload_and_return(image_path: str) -> Dict[str, str]:
|
1170 |
+
"""Helper function to handle image uploads and cleanup"""
|
1171 |
+
try:
|
1172 |
+
if not os.path.exists(image_path):
|
1173 |
+
raise FileNotFoundError(f"Image file not found at {image_path}")
|
1174 |
+
|
1175 |
+
unique_name = f'{uuid.uuid4()}.png'
|
1176 |
+
public_url = await upload_file_to_supabase(image_path, unique_name)
|
1177 |
+
logger.info(f"Uploaded chart: {public_url}")
|
1178 |
+
|
1179 |
+
try:
|
1180 |
+
os.remove(image_path)
|
1181 |
+
except OSError as e:
|
1182 |
+
logger.warning(f"Failed to remove local image file: {e}")
|
1183 |
+
|
1184 |
+
return {"image_url": public_url}
|
1185 |
+
except Exception as e:
|
1186 |
+
logger.error(f"Error in upload_and_return: {e}")
|
1187 |
+
raise e
|
1188 |
+
|
1189 |
+
async def try_generation(method_name: str, method, *args) -> Union[str, None]:
|
1190 |
+
"""Attempt chart generation with a specific method"""
|
1191 |
+
try:
|
1192 |
+
logger.info(f"Attempting chart generation with {method_name}")
|
1193 |
+
result = await asyncio.to_thread(method, *args)
|
1194 |
+
|
1195 |
+
if not result or result == 'Chart not generated':
|
1196 |
+
raise ValueError(f"{method_name} returned empty or invalid result")
|
1197 |
+
|
1198 |
+
if isinstance(result, str):
|
1199 |
+
return result.strip()
|
1200 |
+
elif isinstance(result, list) and result:
|
1201 |
+
return result[0]
|
1202 |
+
|
1203 |
+
raise ValueError(f"{method_name} returned unexpected result type")
|
1204 |
+
except Exception as e:
|
1205 |
+
logger.warning(f"{method_name} failed: {str(e)}")
|
1206 |
+
return None
|
1207 |
+
|
1208 |
+
generation_methods = [
|
1209 |
+
("Raw OpenAI", openai_chart, csv_url, query),
|
1210 |
+
("Raw Groq", groq_chart, csv_url, query),
|
1211 |
+
("LangChain Gemini", lambda u, q: langchain_gemini_csv_handler(u, q, True), csv_url, query),
|
1212 |
+
("LangChain Groq", lambda u, q: langchain_csv_chart(u, q, True), csv_url, query),
|
1213 |
+
]
|
1214 |
+
|
1215 |
+
for attempt, (method_name, method, *args) in enumerate(generation_methods, 1):
|
1216 |
+
try:
|
1217 |
+
result = await try_generation(method_name, method, *args)
|
1218 |
+
if result:
|
1219 |
+
return await upload_and_return(result)
|
1220 |
+
except Exception as e:
|
1221 |
+
logger.error(f"Error processing {method_name}: {e}")
|
1222 |
+
if attempt == len(generation_methods):
|
1223 |
+
logger.error("All chart generation methods failed")
|
1224 |
+
return {"error": "Could not generate chart using any available method"}
|
1225 |
+
|
1226 |
+
return {"error": "All chart generation methods failed"}
|
1227 |
|
1228 |
|
1229 |
+
# Example usage:
|
1230 |
|
1231 |
+
# csv_url = './documents/titanic.csv'
|
1232 |
+
# query = "Create a pie chart of male vs female passengers?"
|
1233 |
+
# result = openai_chart(csv_url, query)
|
1234 |
+
# print(result)
|