Update app.py

app.py

@@ -7,16 +7,15 @@ from langchain_core.prompts import ChatPromptTemplate
 from langchain_groq import ChatGroq
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain.memory import ConversationBufferMemory
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
 from sentence_transformers import SentenceTransformer
 import tavily
-import scipy.stats as stats
 
 class AdvancedRAGChatbot:
     def __init__(self, 
                  tavily_api_key: str,
                  embedding_model: str = "BAAI/bge-large-en-v1.5",
-                 llm_model: str = "llama-3.2-70b-versatile",
+                 llm_model: str = "llama-3.3-70b-versatile",
                  temperature: float = 0.7):
         """Initialize the Advanced RAG Chatbot with Tavily web search integration"""
         # Set the Tavily API key as an environment variable
@@ -33,8 +32,6 @@ class AdvancedRAGChatbot:
         
         # Language Model Configuration
         self.llm = self._configure_llm(llm_model, temperature)
-        self.tokenizer = AutoTokenizer.from_pretrained(llm_model)
-        self.model = AutoModelForCausalLM.from_pretrained(llm_model)
         
         # Conversation Memory
         self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
@@ -69,38 +66,13 @@ class AdvancedRAGChatbot:
             st.error(f"Tavily Search Error: {e}")
             return []
     
-    def _calculate_perplexity(self, text: str) -> float:
-        """Calculate perplexity of the generated text"""
-        inputs = self.tokenizer(text, return_tensors="pt")
-        with torch.no_grad():
-            outputs = self.model(**inputs, labels=inputs["input_ids"])
-            loss = outputs.loss
-        return torch.exp(loss).item()
-    
-    def _calculate_embedding_quality(self, query: str, context: List[str]) -> Dict[float, float]:
-        """Calculate embedding similarity and diversity"""
-        query_embedding = self.semantic_model.encode(query)
-        context_embeddings = self.semantic_model.encode(context)
-        
-        # Cosine similarities
-        similarities = [np.dot(query_embedding, context_emb) / (np.linalg.norm(query_embedding) * np.linalg.norm(context_emb)) 
-                        for context_emb in context_embeddings]
-        
-        return {
-            "mean_similarity": np.mean(similarities),
-            "similarity_variance": np.var(similarities),
-            "min_similarity": np.min(similarities),
-            "max_similarity": np.max(similarities)
-        }
-    
     def process_query(self, query: str) -> Dict[str, Any]:
         """Process the user query with web search and NLP techniques"""
         # Web Search
         web_results = self._tavily_web_search(query)
         
         # Prepare context from web search
-        context = [result.get('content', '') for result in web_results]
-        context_str = "\n\n".join([ 
+        context = "\n\n".join([ 
             f"Title: {result.get('title', 'N/A')}\nContent: {result.get('content', '')}" 
             for result in web_results
         ])
@@ -121,7 +93,7 @@ class AdvancedRAGChatbot:
         Use the following web search results to answer the question precisely:
         
         Web Search Context:
-        {context_str}
+        {context}
         
         Question: {query}
         
@@ -131,18 +103,12 @@ class AdvancedRAGChatbot:
         # Generate Response
         response = self.llm.invoke(full_prompt)
         
-        # Calculate additional metrics
-        perplexity = self._calculate_perplexity(response.content)
-        embedding_metrics = self._calculate_embedding_quality(query, context)
-        
         return {
             "response": response.content,
             "web_sources": web_results,
             "semantic_similarity": semantic_score.tolist(),
             "sentiment": sentiment_result,
-            "named_entities": entities,
-            "perplexity": perplexity,
-            "embedding_metrics": embedding_metrics
+            "named_entities": entities
         }
 
 def main():
@@ -163,7 +129,8 @@ def main():
     
     # Sidebar Configuration
     with st.sidebar:
-        st.header("🔧 Chatbot Metrics & Settings")
+        st.header("🔧 Chatbot Settings")
+        st.markdown("Customize your AI assistant's behavior")
         
         # Model Configuration
         embedding_model = st.selectbox(
@@ -172,12 +139,9 @@ def main():
         )
         temperature = st.slider("Creativity Level", 0.0, 1.0, 0.7, help="Higher values make responses more creative")
         
-        # Metrics Section
+        # Additional Controls
         st.divider()
-        st.subheader("🧮 Performance Metrics")
-        
-        # Placeholders for metrics
-        metrics_container = st.container()
+        st.info("Powered by Tavily Web Search")
     
     # Initialize Chatbot
     chatbot = AdvancedRAGChatbot(
@@ -205,40 +169,19 @@ def main():
             try:
                 response = chatbot.process_query(user_input)
                 
-                # Update Sidebar Metrics
-                with metrics_container:
-                    # Semantic Similarity Metrics
-                    st.metric(
-                        label="🔍 Semantic Similarity Score", 
-                        value=f"{np.mean(response['semantic_similarity']):.4f}",
-                        help="Measures how well the query matches semantic context"
-                    )
-                    
-                    # Embedding Quality Metrics
-                    st.metric(
-                        label="📊 Mean Embedding Similarity", 
-                        value=f"{response['embedding_metrics']['mean_similarity']:.4f}",
-                        delta=f"Variance: {response['embedding_metrics']['similarity_variance']:.4f}"
-                    )
-                    
-                    # Perplexity Metric
-                    st.metric(
-                        label="🧩 Response Perplexity", 
-                        value=f"{response['perplexity']:.2f}",
-                        help="Lower values indicate more predictable and coherent text"
-                    )
-                    
-                    # Sentiment Score
-                    st.metric(
-                        label="😊 Query Sentiment", 
-                        value=response['sentiment']['label'], 
-                        delta=f"{response['sentiment']['score']:.2%}"
-                    )
-                
                 # Bot Response
                 st.markdown("#### AI's Answer")
                 st.write(response['response'])
                 
+                # Sentiment Analysis
+                st.markdown("#### Sentiment Analysis")
+                sentiment = response['sentiment']
+                st.metric(
+                    label="Sentiment", 
+                    value=sentiment['label'], 
+                    delta=f"{sentiment['score']:.2%}"
+                )
+                
                 # Named Entities
                 st.markdown("#### Detected Entities")
                 if response['named_entities']: