Update app.py

app.py

@@ -4,36 +4,313 @@ from twilio.rest import Client
 import os
 import requests
 from PIL import Image
-import io
-import uuid
 import shutil
-from other_function import predict_pest,predict_disease,convert_img,generate_response,ConversationBufferMemory,get_weather,get_rates,get_news
+
+from langchain.vectorstores.chroma import Chroma
+from langchain.prompts import ChatPromptTemplate
+from langchain_community.llms.ollama import Ollama
+from get_embedding_function import get_embedding_function
+from langchain.document_loaders.pdf import PyPDFDirectoryLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.schema.document import Document
+
 app = Flask(__name__)
 UPLOAD_FOLDER = '/code/uploads'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER) # Creates an 'uploads' directory in the current working directory
+
 if not os.path.exists(UPLOAD_FOLDER):
     os.makedirs(UPLOAD_FOLDER)
-    
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+class ConversationBufferMemory:
+    def __init__(self, max_size=6):
+        self.memory = []
+        self.max_size = max_size
+
+    def add_to_memory(self, interaction):
+        self.memory.append(interaction)
+        if len(self.memory) > self.max_size:
+            self.memory.pop(0)  # Remove the oldest interaction
+
+    def get_memory(self):
+        return self.memory
 conversation_memory = ConversationBufferMemory(max_size=2)
 
 account_sid = os.environ.get('TWILIO_ACCOUNT_SID')
 auth_token = os.environ.get('TWILIO_AUTH_TOKEN')
 client = Client(account_sid, auth_token)
-# WhatsApp number to send messages from (your Twilio number)
 from_whatsapp_number = 'whatsapp:+14155238886'
 
+CHROMA_PATH = "chroma"
+DATA_PATH = "data"
+PROMPT_TEMPLATE = """
+Answer the question based only on the following context:
+
+{context}
+
+---
+
+Answer the question based on the above context: {question}
+"""
+
+import os
+from bs4 import BeautifulSoup
+import requests
+from requests.auth import HTTPBasicAuth
+from PIL import Image
+from io import BytesIO
+import pandas as pd
+from urllib.parse import urlparse
+import os
+from pypdf import PdfReader
+from ai71 import AI71
+import os
+
+import pandas as pd
+
+from inference_sdk import InferenceHTTPClient
+import base64
+
+
+
+AI71_API_KEY = os.environ.get('AI71_API_KEY')
+def generate_response(query,chat_history):
+    response = ''
+    for chunk in AI71(AI71_API_KEY).chat.completions.create(
+            model="tiiuae/falcon-180b-chat",
+            messages=[
+                {"role": "system", "content": "You are a best agricultural assistant.Remember to give response not more than 2 sentence.Greet the user if user greets you."},
+                {"role": "user",
+                 "content": f'''Answer the query based on history {chat_history}:{query}'''},
+            ],
+            stream=True,
+    ):
+        if chunk.choices[0].delta.content:
+            response += chunk.choices[0].delta.content
+    return response.replace("###", '').replace('\nUser:','')
+
+def predict_pest(filepath):
+    CLIENT = InferenceHTTPClient(
+        api_url="https://detect.roboflow.com",
+        api_key="oF1aC4b1FBCDtK8CoKx7"
+    )
+    result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1")
+    return result['predictions'][0]
+    
+
+def predict_disease(filepath):
+    CLIENT = InferenceHTTPClient(
+        api_url="https://classify.roboflow.com",
+        api_key="oF1aC4b1FBCDtK8CoKx7"
+    )
+    result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1")
+    return result['predicted_classes'][0]
+
+def convert_img(url, account_sid, auth_token):
+    try:
+        # Make the request to the media URL with authentication
+        response = requests.get(url, auth=HTTPBasicAuth(account_sid, auth_token))
+        response.raise_for_status()  # Raise an error for bad responses
+
+        # Determine a filename from the URL
+        parsed_url = urlparse(url)
+        media_id = parsed_url.path.split('/')[-1]  # Get the last part of the URL path
+        filename = f"downloaded_media_{media_id}"
+
+        # Save the media content to a file
+        media_filepath = os.path.join(UPLOAD_FOLDER, filename)
+        with open(media_filepath, 'wb') as file:
+            file.write(response.content)
+        
+        print(f"Media downloaded successfully and saved as {media_filepath}")
+
+        # Convert the saved media file to an image
+        with open(media_filepath, 'rb') as img_file:
+            image = Image.open(img_file)
+
+            # Optionally, convert the image to JPG and save in UPLOAD_FOLDER
+            converted_filename = f"image.jpg"
+            converted_filepath = os.path.join(UPLOAD_FOLDER, converted_filename)
+            image.convert('RGB').save(converted_filepath, 'JPEG')
+            return converted_filepath
+
+    except requests.exceptions.HTTPError as err:
+        print(f"HTTP error occurred: {err}")
+    except Exception as err:
+        print(f"An error occurred: {err}")
+def get_weather(city):
+  city=city.strip()
+  city=city.replace(' ',"+")
+  r = requests.get(f'https://www.google.com/search?q=weather+in+{city}')
+
+  soup=BeautifulSoup(r.text,'html.parser')
+  temperature=soup.find('div',attrs={'class':'BNeawe iBp4i AP7Wnd'}).text
+  
+  return (temperature)
+
+
+from zenrows import ZenRowsClient
+from bs4 import BeautifulSoup
+Zenrow_api=os.environ.get('Zenrow_api')
+# Initialize ZenRows client with your API key
+client = ZenRowsClient(str(Zenrow_api))
+
+def get_rates():    # URL to scrape
+    url = "https://www.kisandeals.com/mandiprices/ALL/TAMIL-NADU/ALL"
+
+    # Fetch the webpage content using ZenRows
+    response = client.get(url)
+
+    # Check if the request was successful
+    if response.status_code == 200:
+        # Parse the raw HTML content with BeautifulSoup
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Find the table rows containing the data
+        rows = soup.select('table tbody tr')
+        data = {}
+        for row in rows:
+            # Extract commodity and price using BeautifulSoup
+            columns = row.find_all('td')
+            if len(columns) >= 2:
+                commodity = columns[0].get_text(strip=True)
+                price = columns[1].get_text(strip=True)
+                if '₹' in price:
+                    data[commodity] = price
+    return str(data)+" This are the prices for 1 kg"
+
+
+
+
+def get_news(): 
+    news=[]   # URL to scrape
+    url = "https://economictimes.indiatimes.com/news/economy/agriculture?from=mdr"
+
+    # Fetch the webpage content using ZenRows
+    response = client.get(url)
+
+    # Check if the request was successful
+    if response.status_code == 200:
+        # Parse the raw HTML content with BeautifulSoup
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Find the table rows containing the data
+        headlines = soup.find_all("div", class_="eachStory")
+        for story in headlines:
+    # Extract the headline
+            headline = story.find('h3').text.strip()
+            news.append(headline)
+    return news
+
+
+
+def download_and_save_as_txt(url, account_sid, auth_token):
+    try:
+        # Make the request to the media URL with authentication
+        response = requests.get(url, auth=HTTPBasicAuth(account_sid, auth_token))
+        response.raise_for_status()  # Raise an error for bad responses
+
+        # Determine a filename from the URL
+        parsed_url = urlparse(url)
+        media_id = parsed_url.path.split('/')[-1]  # Get the last part of the URL path
+        filename = f"pdf_file.pdf"
+
+        # Save the media content to a .txt file
+        txt_filepath = os.path.join(UPLOAD_FOLDER, filename)
+        with open(txt_filepath, 'wb') as file:
+            file.write(response.content)
+        
+        print(f"Media downloaded successfully and saved as {txt_filepath}")
+        return txt_filepath
+
+    except requests.exceptions.HTTPError as err:
+        print(f"HTTP error occurred: {err}")
+    except Exception as err:
+        print(f"An error occurred: {err}")
+def query_rag(query_text: str):
+    embedding_function = get_embedding_function()
+    db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
+    results = db.similarity_search_with_score(query_text, k=5)
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
+    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+    prompt = prompt_template.format(context=context_text, question=query_text)
+    model = Ollama(model="llama2")
+    response_text = model.invoke(prompt)
+    return response_text
+
+
+def save_pdf_and_update_database(media_url):
+    # Download the PDF file
+    response = requests.get(media_url)
+    pdf_filename = os.path.join(DATA_PATH, f"{uuid.uuid4()}.pdf")
+    with open(pdf_filename, 'wb') as f:
+        f.write(response.content)
+    
+    # Use PyPDFDirectoryLoader if you want to process multiple PDFs in a directory
+    document_loader = PyPDFDirectoryLoader(DATA_PATH)
+    documents = document_loader.load()
+    
+    # The rest of your code remains the same
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=800,
+        chunk_overlap=80,
+        length_function=len,
+        is_separator_regex=False,
+    )
+    chunks = text_splitter.split_documents(documents)
+    
+    add_to_chroma(chunks)
+
+
+def add_to_chroma(chunks: list[Document]):
+    db = Chroma(
+        persist_directory=CHROMA_PATH, embedding_function=get_embedding_function()
+    )
+    chunks_with_ids = calculate_chunk_ids(chunks)
+    existing_items = db.get(include=[])
+    existing_ids = set(existing_items["ids"])
+
+    new_chunks = [chunk for chunk in chunks_with_ids if chunk.metadata["id"] not in existing_ids]
+
+    if new_chunks:
+        new_chunk_ids = [chunk.metadata["id"] for chunk in new_chunks]
+        db.add_documents(new_chunks, ids=new_chunk_ids)
+        db.persist()
+
+
+def calculate_chunk_ids(chunks):
+    last_page_id = None
+    current_chunk_index = 0
+
+    for chunk in chunks:
+        source = chunk.metadata.get("source")
+        page = chunk.metadata.get("page")
+        current_page_id = f"{source}:{page}"
+
+        if current_page_id == last_page_id:
+            current_chunk_index += 1
+        else:
+            current_chunk_index = 0
+
+        chunk_id = f"{current_page_id}:{current_chunk_index}"
+        last_page_id = current_page_id
+
+        chunk.metadata["id"] = chunk_id
+
+    return chunks
+
+
 @app.route('/whatsapp', methods=['POST'])
 def whatsapp_webhook():
     incoming_msg = request.values.get('Body', '').lower()
     sender = request.values.get('From')
-    # Check if an image is attached
     num_media = int(request.values.get('NumMedia', 0))
-    
-    # Get the chat history
+
     chat_history = conversation_memory.get_memory()
-    
+
     if num_media > 0:
         media_url = request.values.get('MediaUrl0')
-        response_text=media_url
+        response_text = media_url
         content_type = request.values.get('MediaContentType0')
         if content_type.startswith('image/'):
             filepath = convert_img(media_url, account_sid, auth_token)
@@ -45,54 +322,43 @@ def whatsapp_webhook():
                 pest = predict_pest(filepath)
             except:
                 pest = None
-        
+
             if disease:
                 response_text = f"Detected disease: {disease}"
-                # Generate additional insights about the disease
                 disease_info = generate_response(f"Provide brief information about {disease} in plants", chat_history)
                 response_text += f"\n\nAdditional information: {disease_info}"
             elif pest:
                 response_text = f"Detected pest: {pest}"
-                # Generate additional insights about the pest
                 pest_info = generate_response(f"Provide brief information about {pest} in agriculture", chat_history)
                 response_text += f"\n\nAdditional information: {pest_info}"
             else:
                 response_text = "Please upload another image with good quality."
-            
+        elif content_type == "application/pdf":
+            # Process the PDF and update the database
+            save_pdf_and_update_database(media_url)
+            response_text = "Your PDF has been saved and processed."
         else:
             filepath = download_and_save_as_txt(media_url, account_sid, auth_token)
-            #Rag code is as follows.Provide filepath inplace of pdf..
-
-
-
-
-            
-
-
-
-            
-    elif ('weather' in incoming_msg.lower()) or ('climate' in incoming_msg.lower()) or ('temperature' in incoming_msg.lower()):
-        response_text=get_weather(incoming_msg.lower())
+            response_text = query_rag(filepath)
+    elif ('weather' in incoming_msg.lower()) or ('climate' in incoming_msg.lower()) or (
+            'temperature' in incoming_msg.lower()):
+        response_text = get_weather(incoming_msg.lower())
     elif 'bookkeeping' in incoming_msg:
         response_text = "Please provide the details you'd like to record."
-    elif ('rates' in incoming_msg.lower()) or ('price' in incoming_msg.lower()) or ('market' in incoming_msg.lower()) or ('rate' in incoming_msg.lower()) or ('prices' in incoming_msg.lower()):
-        rates=get_rates()
-        response_text = generate_response(incoming_msg+'data is '+rates, chat_history)
+    elif ('rates' in incoming_msg.lower()) or ('price' in incoming_msg.lower()) or (
+            'market' in incoming_msg.lower()) or ('rate' in incoming_msg.lower()) or ('prices' in incoming_msg.lower()):
+        rates = get_rates()
+        response_text = generate_response(incoming_msg + ' data is ' + rates, chat_history)
     elif ('news' in incoming_msg.lower()) or ('information' in incoming_msg.lower()):
-        news=get_news()
-        response_text = generate_response(incoming_msg+'data is '+str(news), chat_history)
-        
+        news = get_news()
+        response_text = generate_response(incoming_msg + ' data is ' + str(news), chat_history)
     else:
-        # Generate response considering the chat history
         response_text = generate_response(incoming_msg, chat_history)
-    
-    # Add the interaction to memory
+
     conversation_memory.add_to_memory({"user": incoming_msg, "assistant": response_text})
-    
     send_message(sender, response_text)
     return '', 204
-def get_agricultural_insights(query):
-    return generate_response(query)
+
 
 def send_message(to, body):
     try:
@@ -105,15 +371,15 @@ def send_message(to, body):
     except Exception as e:
         print(f"Error sending message: {e}")
 
-# Function to send an initial message
+
 def send_initial_message(to_number):
     send_message(
         f'whatsapp:{to_number}',
         'Welcome to the Agri AI Chatbot! How can I assist you today?'
     )
 
+
 if __name__ == '__main__':
-    send_initial_message('916382792828')
+    #send_initial_message('916382792828')
     send_initial_message('919080522395')
-    
-    app.run(host='0.0.0.0',  port=7860)
+    app.run(host='0.0.0.0', port=7860)