Updated app.py to version 7

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import pandas as pd
-from huggingface_hub import InferenceClient
+import requests
 
 # Page configuration
 st.set_page_config(
@@ -11,23 +11,50 @@ st.set_page_config(
 )
 
 st.title("📊 LLM Data Analyzer")
-st.write("*Analyze data and chat with AI powered by Hugging Face Inference API*")
+st.write("*Analyze data and chat with AI powered by Hugging Face*")
 
-# Initialize HF Inference Client
-@st.cache_resource
-def get_hf_client():
-    """Get Hugging Face Inference Client"""
-    try:
-        return InferenceClient()
-    except Exception as e:
-        st.error(f"Error initializing HF client: {e}")
-        return None
+# Get HF token from environment
+import os
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-client = get_hf_client()
+if not HF_TOKEN:
+    st.warning("⚠️ HF_TOKEN environment variable not set. Features may be limited.")
 
-if client is None:
-    st.error("Failed to initialize Hugging Face client")
-    st.stop()
+# Function to call HF API with new endpoint
+def call_hf_api(prompt):
+    """Call Hugging Face Inference API using new router endpoint"""
+    try:
+        headers = {
+            "Authorization": f"Bearer {HF_TOKEN}" if HF_TOKEN else "",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "inputs": prompt,
+            "parameters": {
+                "max_new_tokens": 300,
+                "temperature": 0.7,
+            }
+        }
+        
+        # Use new HF router endpoint
+        response = requests.post(
+            "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1",
+            headers=headers,
+            json=payload,
+            timeout=30
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            if isinstance(result, list) and len(result) > 0:
+                return result[0].get("generated_text", "")
+            return str(result)
+        else:
+            return f"Error: {response.status_code} - {response.text}"
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Create tabs
 tab1, tab2, tab3 = st.tabs(["📤 Upload & Analyze", "💬 Chat", "📊 About"])
@@ -81,9 +108,8 @@ with tab1:
             
             if question:
                 with st.spinner("🤔 AI is analyzing your data..."):
-                    try:
-                        data_summary = df.describe().to_string()
-                        prompt = f"""You are a data analyst expert. You have the following data summary:
+                    data_summary = df.describe().to_string()
+                    prompt = f"""You are a data analyst expert. You have the following data summary:
 
 {data_summary}
 
@@ -92,18 +118,14 @@ Column names: {', '.join(df.columns.tolist())}
 User's question: {question}
 
 Please provide a clear, concise analysis based on the data summary."""
-                        
-                        # Use Hugging Face Inference API
-                        response = client.text_generation(
-                            prompt,
-                            max_new_tokens=300,
-                            temperature=0.7,
-                        )
-                        
+                    
+                    response = call_hf_api(prompt)
+                    
+                    if response.startswith("Error"):
+                        st.error(response)
+                    else:
                         st.success("✅ Analysis Complete")
                         st.write(response)
-                    except Exception as e:
-                        st.error(f"Error analyzing data: {e}")
         
         except Exception as e:
             st.error(f"Error reading file: {e}")
@@ -124,7 +146,7 @@ with tab2:
         with st.chat_message(message["role"]):
             st.markdown(message["content"])
     
-    # Chat input - MUST be outside tabs, so we use text_input instead
+    # Chat input
     user_input = st.text_input(
         "Type your message:",
         placeholder="Ask me anything...",
@@ -137,15 +159,12 @@ with tab2:
         
         # Generate AI response
         with st.spinner("⏳ Generating response..."):
-            try:
-                prompt = f"User: {user_input}\n\nAssistant:"
-                
-                response = client.text_generation(
-                    prompt,
-                    max_new_tokens=300,
-                    temperature=0.7,
-                )
-                
+            prompt = f"User: {user_input}\n\nAssistant:"
+            response = call_hf_api(prompt)
+            
+            if response.startswith("Error"):
+                st.error(response)
+            else:
                 assistant_message = response.strip()
                 
                 # Add assistant message to history
@@ -156,8 +175,6 @@ with tab2:
                 
                 # Rerun to display the new messages
                 st.rerun()
-            except Exception as e:
-                st.error(f"Error generating response: {e}")
 
 # ============================================================================
 # TAB 3: About
@@ -174,6 +191,7 @@ with tab3:
     
     - **Framework:** Streamlit
     - **AI Engine:** Hugging Face Inference API
+    - **Model:** Mistral 7B Instruct
     - **Hosting:** Hugging Face Spaces (Free Tier)
     - **Language:** Python
     
@@ -194,6 +212,7 @@ with tab3:
     
     - [Hugging Face](https://huggingface.co/) - AI models and hosting
     - [Streamlit](https://streamlit.io/) - Web framework
+    - [Mistral AI](https://mistral.ai/) - 7B Language Model
     
     ### 📖 Quick Tips