Upload 2 files

app.py

@@ -1,21 +1,24 @@
 import gradio as gr
-from langchain_community.chat_models import ChatOpenAI
+from langchain_openai import ChatOpenAI
 from langchain.prompts import PromptTemplate
-from langchain.agents import initialize_agent, AgentType, Tool
-from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
-from langchain_community.callbacks import get_openai_callback  # Import the callback
+from langchain.agents import AgentExecutor, create_structured_chat_agent
+from langchain_community.callbacks import get_openai_callback
 import os
 import time
-import json
-
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain import hub
 
 # Load environment variables
 API_KEY = os.getenv("OPENAI_API_KEY")
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+PASSWORD = os.getenv("APP_PASSWORD")
+
 if not API_KEY:
     raise ValueError("Please set the OPENAI_API_KEY environment variable.")
 
-# Set the password for accessing the app
-PASSWORD = os.getenv("APP_PASSWORD")
+if not TAVILY_API_KEY:
+    raise ValueError("Please set the TAVILY_API_KEY environment variable.")
+
 if not PASSWORD:
     raise ValueError("Please set the APP_PASSWORD environment variable.")
 
@@ -23,7 +26,7 @@ if not PASSWORD:
 prompt_template = PromptTemplate(
     input_variables=["marka_model", "automobil", "question"],
     template="""
-Respond in systematical cleaned-up text format and only in Russian: You are a virtual technical expert with years of experience in vehicle repair and maintenance. Your responses are intended for professional mechanics and contain only reliable and safe instructions that adhere to best practices and manufacturer recommendations.
+Respond in systematically cleaned-up Markdown format and only in Russian: You are a virtual technical expert with years of experience in vehicle repair and maintenance. Your responses are intended for professional mechanics and contain only reliable and safe instructions that adhere to best practices and manufacturer recommendations.
 
 We are currently discussing repair work for the {marka_model} model vehicle {automobil}. Please provide your expert opinion on the following question: {question}
 
@@ -49,66 +52,65 @@ Balance confidence with caution to ensure the safety and reliability of the comp
 """
 )
 
-
 # Initialize components with updated classes
 llm = ChatOpenAI(
     openai_api_key=API_KEY,
-    model_name="gpt-4o-mini",
-    temperature=0.5 ,
+    model_name="gpt-4",
+    temperature=0,
 )
 
-duckduckgo_search = DuckDuckGoSearchAPIWrapper()
-fact_check_tool = Tool(
-    name="DuckDuckGo Search",
-    func=duckduckgo_search.run,
-    description="Useful for fact-checking information using DuckDuckGo search. Input should be a search query."
-)
+tools = [TavilySearchResults(max_results=5)]
+prompt = prompt_template
 
-# Initialize the agent with the new method
-agent = initialize_agent(
-    tools=[fact_check_tool],
-    llm=llm,
-    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=True,
-    max_iterations=10,
-)
+# Initialize the structured chat agent
+prompt = hub.pull("hwchase17/structured-chat-agent")
+agent = create_structured_chat_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools)
 
 def generate_response(marka_model, automobil, question, password):
+    """
+    Generates a response based on the user's automotive repair question.
+
+    Args:
+        marka_model (str): Car make and model.
+        automobil (str): Vehicle details.
+        question (str): Automotive repair question.
+        password (str): Password for accessing the app.
+
+    Returns:
+        tuple: Response in Markdown format and performance metrics in JSON.
+    """
     if password != PASSWORD:
         return {"error": "Incorrect password. Access denied."}
     
-    with get_openai_callback() as cb:
-        start_time = time.perf_counter()
-        user_input = {
-            "input": {
+    try:
+        with get_openai_callback() as cb:
+            start_time = time.perf_counter()
+            user_input = {  
                 "marka_model": marka_model,
                 "automobil": automobil,
                 "question": question
             }
+            formatted_prompt = prompt_template.format(**user_input)
+            response = agent_executor.invoke({"input": formatted_prompt})
+            response = llm.invoke(f'Clean up the response and translate it to Russian if needed: {response}').content
+            end_time = time.perf_counter()
+    
+        total_time = end_time - start_time
+    
+        # Updated cost rates
+        costs = {'input': 0.150 / 1e6, 'output': 0.600 / 1e6}
+    
+        total_cost = cb.prompt_tokens * costs['input'] + cb.completion_tokens * costs['output']
+    
+        json_data = {
+            "total_cost": round(total_cost, 6),
+            "total_time_seconds": round(total_time, 4)
         }
-        response = agent.run(user_input)
-        # response = llm.invoke(f'Clean up the response and translate it to Russian if needed: {response}').content
-        end_time = time.perf_counter()
-
-    total_time = end_time - start_time
-
-    # Updated cost rates
-    costs = {'input': 0.150 / 1e6, 'output': 0.600 / 1e6}
-
-    total_cost = cb.prompt_tokens * costs['input'] + cb.completion_tokens * costs['output']
-
-    json_data = {
-        "response": response,
-        # "total_tokens": cb.total_tokens,
-        # "prompt_tokens": cb.prompt_tokens,
-        # "completion_tokens": cb.completion_tokens,
-        "total_cost": round(total_cost, 6),
-        "total_time_seconds": round(total_time, 4)
-    }
-
-    # Format response as Markdown
     
-    return  json_data
+        return response, json_data
+    except Exception as e:
+        return {"error": str(e)}, {}
 
 # Define example inputs
 examples = [
@@ -145,10 +147,10 @@ iface = gr.Interface(
         gr.Textbox(label="Car Make and Model", placeholder="Enter the car make and model"),
         gr.Textbox(label="Vehicle", placeholder="Enter the vehicle"),
         gr.Textbox(label="Your Question", placeholder="Enter your automotive repair question"),
-        gr.Textbox(label="Password", placeholder="Enter the password to access the app"),
+        gr.Password(label="Password", placeholder="Enter the password to access the app"),
     ],
     outputs=[
-        # gr.Markdown(label="Answer"),
+        gr.Markdown(label="Answer"),
         gr.JSON(label="Performance Metrics")
     ],
     title="Automotive Technical Assistant",

requirements.txt

@@ -3,4 +3,5 @@ requests
 langchain
 langchain-community
 openai
-duckduckgo_search 
+tavily-python
+