Update app.py

app.py

@@ -1,443 +1,3 @@
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# """
-# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-# """
-# client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct")
-
-
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens,
-#     temperature,
-#     top_p,
-# ):
-#     messages = [{"role": "system", "content": system_message}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(
-#     respond,
-#     additional_inputs=[
-#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
-#     ],
-# )
-
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-# client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct")
-
-# def respond(message, history: list[tuple[str, str]]):
-#     system_message = (
-#     "You are a helpful and experienced coding assistant specialized in web development. "
-#     "Help the user by generating complete and functional code for building websites. "
-#     "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. "
-#     "Break down the tasks clearly if needed, and be friendly and supportive in your responses.")
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     messages = [{"role": "system", "content": system_message}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(respond)
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# """
-# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-# """
-# client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct")
-
-# def respond(message, history: list[tuple[str, str]]):
-#     system_message = (
-#         "You are a helpful and experienced coding assistant specialized in web development. "
-#         "Help the user by generating complete and functional code for building websites. "
-#         "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. "
-#         "Break down the tasks clearly if needed, and be friendly and supportive in your responses."
-#     )
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     messages = [{"role": "system", "content": system_message}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(respond)
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# # 1. Instantiate with named model param
-# client = InferenceClient(model="Qwen/Qwen2.5-Coder-32B-Instruct")
-
-# def respond(message, history: list[tuple[str, str]]):
-#     system_message = (
-#         "You are a helpful and experienced coding assistant specialized in web development. "
-#         "Help the user by generating complete and functional code for building websites. "
-#         "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
-#         "based on their requirements."
-#     )
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     # Build messages in OpenAI-compatible format
-#     messages = [{"role": "system", "content": system_message}]
-#     for user_msg, assistant_msg in history:
-#         if user_msg:
-#             messages.append({"role": "user", "content": user_msg})
-#         if assistant_msg:
-#             messages.append({"role": "assistant", "content": assistant_msg})
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-#     # 2. Use named parameters and alias if desired
-#     for chunk in client.chat.completions.create(
-#         model="Qwen/Qwen2.5-Coder-32B-Instruct",
-#         messages=messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         # 3. Extract token content
-#         token = chunk.choices[0].delta.content or ""
-#         response += token
-#         yield response
-
-# # 4. Wire up Gradio chat interface
-# demo = gr.ChatInterface(respond, type="messages")
-
-# if __name__ == "__main__":
-#     demo.launch()
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# hf_token = "HF_TOKEN"
-
-
-# # Ensure token is available
-# if hf_token is None:
-#     raise ValueError("HUGGINGFACEHUB_API_TOKEN is not set in .env file or environment.")
-
-# # Instantiate Hugging Face Inference Client with token
-# client = InferenceClient(
-#     model="Qwen/Qwen2.5-Coder-32B-Instruct",
-#     token=hf_token
-# )
-
-# def respond(message, history: list[tuple[str, str]]):
-#     system_message = (
-#         "You are a helpful and experienced coding assistant specialized in web development. "
-#         "Help the user by generating complete and functional code for building websites. "
-#         "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
-#         "based on their requirements."
-#     )
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     # Build conversation history
-#     messages = [{"role": "system", "content": system_message}]
-#     for user_msg, assistant_msg in history:
-#         if user_msg:
-#             messages.append({"role": "user", "content": user_msg})
-#         if assistant_msg:
-#             messages.append({"role": "assistant", "content": assistant_msg})
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-#     # Stream the response from the model
-#     for chunk in client.chat.completions.create(
-#         model="Qwen/Qwen2.5-Coder-32B-Instruct",
-#         messages=messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = chunk.choices[0].delta.content or ""
-#         response += token
-#         yield response
-
-# # Gradio UI
-# demo = gr.ChatInterface(respond, type="messages")
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-# import gradio as gr
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-# import torch
-
-# # Load once globally
-# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
-# model = AutoModelForCausalLM.from_pretrained(
-#     "Qwen/Qwen2.5-Coder-32B-Instruct",
-#     device_map="auto",
-#     torch_dtype=torch.float16,
-# )
-
-# def respond(message, history):
-#     system_prompt = (
-#         "You are a helpful coding assistant specialized in web development. "
-#         "Provide complete code snippets for HTML, CSS, JS, Flask, Node.js etc."
-#     )
-#     # Build input prompt including chat history
-#     chat_history = ""
-#     for user_msg, bot_msg in history:
-#         chat_history += f"User: {user_msg}\nAssistant: {bot_msg}\n"
-#     prompt = f"{system_prompt}\n{chat_history}User: {message}\nAssistant:"
-
-#     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-#     outputs = model.generate(
-#         **inputs,
-#         max_new_tokens=512,
-#         temperature=0.7,
-#         do_sample=True,
-#         top_p=0.95,
-#         eos_token_id=tokenizer.eos_token_id,
-#     )
-#     generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-#     # Extract only the new response part after the prompt
-#     response = generated_text[len(prompt):].strip()
-
-#     # Append current Q/A to history
-#     history.append((message, response))
-#     return "", history
-
-# demo = gr.ChatInterface(respond, type="messages")
-
-# if __name__ == "__main__":
-#     demo.launch()
-# import os
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-# from dotenv import load_dotenv
-
-# # Load .env variables (make sure to have HF_TOKEN in .env or set as env var)
-# load_dotenv()
-# HF_TOKEN = os.getenv("HF_TOKEN")  # or directly assign your token here as string
-
-# # Initialize InferenceClient with Hugging Face API token
-# client = InferenceClient(
-#     model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
-#     token=HF_TOKEN
-# )
-
-# def respond(message, history):
-#     """
-#     Chat response generator function streaming from Hugging Face Inference API.
-#     """
-#     system_message = (
-#         "You are a helpful and experienced coding assistant specialized in web development. "
-#         "Help the user by generating complete and functional code for building websites. "
-#         "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
-#         "based on their requirements."
-#     )
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     # Prepare messages in OpenAI chat format
-#     messages = [{"role": "system", "content": system_message}]
-#     for user_msg, assistant_msg in history:
-#         if user_msg:
-#             messages.append({"role": "user", "content": user_msg})
-#         if assistant_msg:
-#             messages.append({"role": "assistant", "content": assistant_msg})
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-#     # Stream response tokens from Hugging Face Inference API
-#     for chunk in client.chat.completions.create(
-#         model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
-#         messages=messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = chunk.choices[0].delta.get("content", "")
-#         response += token
-#         yield response
-
-# # Create Gradio chat interface
-# demo = gr.ChatInterface(fn=respond, title="Website Building Assistant")
-
-# if __name__ == "__main__":
-#     demo.launch()
-# import os
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-# from dotenv import load_dotenv
-
-# # Load environment variables
-# load_dotenv()
-# HF_TOKEN = os.getenv("HF_TOKEN")  # Ensure this is set in .env
-
-# # Initialize Hugging Face Inference Client
-# client = InferenceClient(
-#     model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
-#     token=HF_TOKEN
-# )
-
-# # Define system instructions for the chatbot
-# system_message = (
-#     "You are a helpful and experienced coding assistant specialized in web development. "
-#     "Help the user by generating complete and functional code for building websites. "
-#     "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
-#     "based on their requirements."
-# )
-
-# # Define the response generation function
-# def respond(message, history):
-#     max_tokens = 2048
-#     temperature = 0.7
-#     top_p = 0.95
-
-#     # Convert chat history into OpenAI-style format
-#     messages = [{"role": "system", "content": system_message}]
-#     for item in history:
-#         role = item["role"]
-#         content = item["content"]
-#         messages.append({"role": role, "content": content})
-    
-#     # Add the latest user message
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     # Streaming response from the Hugging Face Inference API
-#     for chunk in client.chat.completions.create(
-#         model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
-#         messages=messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = chunk.choices[0].delta.get("content")
-#         if token is not None:
-#             response += token
-#             yield response
-
-# # Create Gradio Chat Interface
-# demo = gr.ChatInterface(
-#     fn=respond,
-#     title="Website Building Assistant",
-#     chatbot=gr.Chatbot(show_label=False),
-#     type="openai",  # Use OpenAI-style message format
-# )
-
-# if __name__ == "__main__":
-#     demo.launch()# app.py
-
-# app.py
-
-# app.py
-
 # import os
 # import gradio as gr
 # from huggingface_hub import InferenceClient
@@ -449,7 +9,7 @@ For more information on `huggingface_hub` Inference API support, please check th
 
 # # Initialize Hugging Face Inference Client
 # client = InferenceClient(
-#     model="mistralai/Codestral-22B-v0.1",
+#     model="mistralai/Mistral-7B-Instruct-v0.3",
 #     token=HF_TOKEN
 # )
 
@@ -461,7 +21,7 @@ For more information on `huggingface_hub` Inference API support, please check th
 #     "based on their requirements."
 # )
 
-# # Streaming chatbot logic using chat.completions
+# # Streaming chatbot logic
 # def respond(message, history):
 #     # Prepare messages with system prompt
 #     messages = [{"role": "system", "content": system_message}]
@@ -472,7 +32,7 @@ For more information on `huggingface_hub` Inference API support, please check th
 #     # Stream response from the model
 #     response = ""
 #     for chunk in client.chat.completions.create(
-#         model="mistralai/Codestral-22B-v0.1",
+#         model="mistralai/Mistral-7B-Instruct-v0.3",
 #         messages=messages,
 #         max_tokens=1024,
 #         temperature=0.7,
@@ -492,9 +52,6 @@ For more information on `huggingface_hub` Inference API support, please check th
 # if __name__ == "__main__":
 #     demo.launch()
 
-
-# app.py
-
 import os
 import gradio as gr
 from huggingface_hub import InferenceClient
@@ -506,7 +63,7 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 
 # Initialize Hugging Face Inference Client
 client = InferenceClient(
-    model="mistralai/Mistral-7B-Instruct-v0.3",
+    model="Qwen/Qwen2.5-Coder-7B-Instruct",
     token=HF_TOKEN
 )
 
@@ -522,21 +79,27 @@ system_message = (
 def respond(message, history):
     # Prepare messages with system prompt
     messages = [{"role": "system", "content": system_message}]
-    for msg in history:
-        messages.append(msg)
+    for user_msg, assistant_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": assistant_msg})
     messages.append({"role": "user", "content": message})
 
     # Stream response from the model
     response = ""
     for chunk in client.chat.completions.create(
-        model="mistralai/Mistral-7B-Instruct-v0.3",
+        model="Qwen/Qwen2.5-Coder-7B-Instruct",
         messages=messages,
-        max_tokens=1024,
+        max_tokens=2048,
         temperature=0.7,
         top_p=0.95,
         stream=True,
     ):
-        token = chunk.choices[0].delta.get("content", "") or ""
+        # Safely handle empty choices
+        if not chunk.choices:
+            continue
+            
+        # Safely extract token content
+        token = chunk.choices[0].delta.content or ""
         response += token
         yield response