Update app.py

app.py

@@ -11,7 +11,7 @@ import wikipediaapi
 #from typing import Optional
 #from termax.agent import Agent  # Assuming you're using a specific agent framework
 #from termax.tool import google_search  # Assuming this is your search tool import
-from langchain_google_genai import ChatGoogleGenerativeAI
+#from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.agents import AgentExecutor, create_tool_calling_agent
 from langchain.tools import Tool
 from langchain import hub
@@ -55,155 +55,7 @@ class BasicAgent:
         response = requests.get(url)
         return response.text
 
-        
-    def search_tool(self, prompt: str) -> str:
-        model = genai.GenerativeModel('gemini-pro')
-        response = model.generate_content(
-            f"Search the web for: {prompt}. Summarize the results."
-            )
-        return response.text
-
-    def search_tool(self, prompt: str) -> str:
-        # Initialize Gemini model
-        llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")  # or "gemini-pro"
-        
-        # Define Google Search tool (replace with actual search API)
-        def google_search(query: str) -> str:
-            # Use SerpAPI, Google Custom Search, etc.
-            return f"Search results for: {query}"
-        
-        tools = [
-            Tool(
-                name="google_search",
-                func=google_search,
-                description="Useful for searching the web."
-            )
-        ]
-        
-        # Create agent
-        agent = create_tool_calling_agent(llm, tools, prompt_template=hub.pull("hwchase17/openai-tools-agent"))
-        agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-        
-        # Run the agent
-        response = agent_executor.invoke({"input": prompt})
-        return response["output"]
-
-
-    def search_tool(self, prompt: str) -> str:
-        """
-        Searches for information using a Gemini-powered agent with Google Search capability.
-        
-        Args:
-            prompt: The user's query to search for
-            
-        Returns:
-            str: The response from the search agent
-        """
-        root_agent = Agent(
-            name="search_assistant",
-            model="gemini-2.0-flash",  # Or your preferred Gemini model
-            instruction="You are a helpful assistant. Answer user questions using Google Search when needed.",
-            description="An assistant that can search the web.",
-            tools=[google_search]
-        )
-        
-        # Actually invoke the agent with the prompt
-        response = root_agent.run(prompt)
-        return response
-
-    def answer_question(self, prompt: str) -> str:
-        from google.genarativeai import genai
-
-        client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
-        
-        response = client.models.generate_content(
-            model="gemini-2.0-flash",
-            contents=prompt,
-        )        
-        return response.text
-
-
-    def generate_response(self, prompt: str) -> str:
-        """Get response from HuggingFace model"""
-        try:
-            response = requests.post(
-                self.api_url,
-                headers=self.headers,
-                json={"inputs": prompt}
-            )
-            response.raise_for_status()
-            return response.json()[0]['generated_text']
-        except Exception as e:
-            return f"Error generating response: {str(e)}"
-
-    def web_search(self, query: str) -> List[Dict]:
-        """Search using SearxNG (meta-search engine)"""
-        params = {
-            "q": query,
-            "format": "json",
-            "engines": "google,bing,duckduckgo"
-        }
-        try:
-            response = requests.get(self.searx_url, params=params)
-            response.raise_for_status()
-            return response.json().get("results", [])
-        except requests.RequestException:
-            return []
-
-    def wikipedia_search(self, query: str) -> str:
-        """Get Wikipedia summary"""
-        page = self.wiki.page(query)
-        return page.summary if page.exists() else "No Wikipedia page found"
-
-   
-    def _process_pdf(self, file_path: str) -> str:
-        """Fallback PDF text extraction (works for simple PDFs)."""
-        try:
-            # Read raw binary data and decode text (very basic)
-            with open(file_path, 'rb') as f:
-                text = f.read().decode('latin-1')  # Try UTF-8 if this fails
-                return text if text.strip() else "PDF text not extractable (install pdfminer.six for better results)"
-        except Exception as e:
-            return f"PDF read error: {str(e)}"
-
-
-    def __call__(self, query: str) -> str:
-        """Handle queries (text, search, or file processing)"""
-        print(f"Processing query: {query[:50]}...")
-        
-        # If it's a file path, process it
-        if os.path.exists(query):
-            return self.process_document(query)
-        
-        # If it's a Wikipedia-style query (e.g., "wikipedia:Python")
-        if query.lower().startswith("wikipedia:"):
-            topic = query.split(":")[1].strip()
-            return self.wikipedia_search(topic)
-        
-        # If it's a web search (e.g., "search:best LLMs 2024")
-        if query.lower().startswith("search:"):
-            search_query = query.split(":")[1].strip()
-            results = self.web_search(search_query)
-            return "\n".join([f"{r['title']}: {r['url']}" for r in results])
-        
-        # Default: Use HuggingFace for text generation
-        return self.generate_response(query)
-
-# Example Usage
-if __name__ == "__main__":
-    agent = BasicAgent()
-    
-    # Test Wikipedia search
-    print(agent("wikipedia:Python"))
-    
-    # Test web search (requires SearxNG instance)
-    # print(agent("search:best programming languages 2024"))
-    
-    # Test text generation
-    print(agent("Explain quantum computing in simple terms"))
-    
-    # Test file processing (example: PDF)
-    # print(agent("/path/to/document.pdf"))
+      
     
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):