Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 from duckduckgo_search import DDGS
-from typing import List, Dict, Tuple
-from huggingface_hub import InferenceClient
+from typing import List, Dict
 import os
 
 # Environment variables and configurations
@@ -36,87 +35,32 @@ def summarize_results(query: str, search_results: List[Dict[str, str]], model: s
         
         Article:"""
 
-        
         summary = DDGS().chat(prompt, model="llama-3-70b")
         return summary
     except Exception as e:
         return f"An error occurred during summarization: {str(e)}"
 
-def rephrase_query(previous_response: str, new_query: str) -> str:
-    client = InferenceClient(
-        "mistralai/Mistral-7B-Instruct-v0.3",
-        token=huggingface_token,
-    )
-    
-    if previous_response:
-        prompt = f"""Analyze the following previous response to understand the context:
-    
-        {previous_response}
-    
-        Now, given the new query:
-    
-        {new_query}
-    
-        Rephrase the new query to ensure it aligns with the context of the previous response. The rephrased query should be specific, concise, and optimized for a web search. Provide the rephrased query as a single-line response:"""
-    else:
-        prompt = f"""Given the new query:
-    
-        {new_query}
-    
-        Rephrase this query to make it more specific, concise, and optimized for a web search. Provide the rephrased query as a single-line response:"""
-
-
-    response = client.text_generation(prompt, max_new_tokens=30, temperature=0.3)
-    
-    # Extract only the rephrased query from the response
-    rephrased_query = response.split("\n")[-1].strip()
-    return rephrased_query
-
 def respond(message, chat_history, model, temperature, num_api_calls):
-    rephrased_query = message
-    if chat_history:
-        previous_response = chat_history[-1][1]
-        rephrased_query = rephrase_query(previous_response, message)
-    
-    print(f"Initial Rephrased Query: {rephrased_query}")
-
     final_summary = ""
     for _ in range(num_api_calls):
-        if not rephrased_query or not isinstance(rephrased_query, str):
-            print(f"Invalid rephrased query: {rephrased_query}")
-            break
-
-        search_results = get_web_search_results(rephrased_query)
-        
-        # If no results or error, try rephrasing and searching again
-        if not search_results or "error" in search_results[0]:
-            print(f"No results found for: {rephrased_query}. Attempting to rephrase.")
-            rephrased_query = rephrase_query("", rephrased_query)  # Rephrase without context
-            print(f"New Rephrased Query: {rephrased_query}")
-            search_results = get_web_search_results(rephrased_query)
+        search_results = get_web_search_results(message)
         
         if not search_results:
-            final_summary += f"No search results found for the query: {rephrased_query}\n\n"
+            final_summary += f"No search results found for the query: {message}\n\n"
         elif "error" in search_results[0]:
             final_summary += search_results[0]["error"] + "\n\n"
         else:
-            summary = summarize_results(rephrased_query, search_results, model)
+            summary = summarize_results(message, search_results, model)
             final_summary += summary + "\n\n"
 
     return final_summary if final_summary else "Unable to generate a response. Please try a different query."
 
-#def initial_conversation():
-#    return [
-#        (None, "Welcome! I'm your AI-powered Web Search and PDF Chat Assistant. I can help you find information on the web, summarize content, and analyze PDF documents. What would you like to know?")
-#    ]
-
 css = """
 Your custom CSS here
 """
 
 custom_placeholder = "Ask me anything about web content"
 
-# Define the theme
 theme = gr.themes.Soft(
     primary_hue="orange",
     secondary_hue="amber",
@@ -136,7 +80,6 @@ theme = gr.themes.Soft(
     code_background_fill_dark="#140b0b"
 )
 
-# Apply the theme to the ChatInterface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -161,7 +104,6 @@ demo = gr.ChatInterface(
         likeable=True,
         layout="bubble",
         height=400,
-#        value=initial_conversation()
     )
 )