Update app.py

app.py

@@ -7,8 +7,10 @@ from transformers import pipeline
 # Configure logging
 logging.basicConfig(level=logging.DEBUG)
 
+# Set Hugging Face token
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf_token
 # Load Hugging Face model
-MODEL_NAME = "mistralai/Mistral-Large-Instruct-2407"  # Hugging Face Model name
+MODEL_NAME = "mistralai/Mistral-Large-Instruct-2407"
 llm_pipeline = pipeline("text-generation", model=MODEL_NAME)
 
 # Load datasets
@@ -38,16 +40,6 @@ fashion_keywords = [
     'jackets', 'sweaters', 'suits', 'accessories', 't-shirts'
 ]
 
-# LLM-based function
-def query_llm(prompt):
-    """Query the LLM for responses."""
-    try:
-        responses = llm_pipeline(prompt, max_length=150, num_return_sequences=1)
-        return responses[0]['generated_text'].strip()
-    except Exception as e:
-        logging.error(f"Error querying the LLM: {e}")
-        return "Sorry, I'm having trouble processing your request right now."
-
 def determine_category(query):
     """Determine the category based on the query."""
     query_lower = query.lower()
@@ -61,7 +53,84 @@ def determine_category(query):
         logging.debug(f"Query '{query}' categorized as 'general'.")
         return 'general'
 
-# Fetch response based on query
+def format_electronics_response(row):
+    """Format response using data from an Electronics DataFrame row."""
+    response = (
+        f"**Product Name:** {row['ProductName']}\n\n"
+        f"**Description:**\n{row['Description']}\n\n"
+        f"**Price:** ${row['Price']}\n"
+        f"**Brand:** {row['Brand']}\n"
+        f"**Model:** {row['Model']}\n"
+        f"**Department:** {row['Department']}\n\n"
+        f"**Reviews:**\n{row['Reviews']}\n\n"
+        f"**Ratings:** {row['Ratings']} / 5\n"
+    )
+    return response
+
+def format_fashion_response(row):
+    """Format response using data from a Fashion DataFrame row."""
+    response = (
+        f"**Product Name:** {row['Name']}\n\n"
+        f"**Description:**\n{row['Description']}\n\n"
+        f"**Price:** ${row['Price']}\n"
+        f"**Brand:** {row['Brand']}\n"
+        f"**Model:** {row['Model']}\n\n"
+        f"**Rating:** {row['Rating']} / 5\n"
+    )
+    return response
+
+def extract_filters(query):
+    """Extract filters from the user's query."""
+    filters = {}
+    query_lower = query.lower()
+
+    if 'best' in query_lower:
+        if 'rating' in query_lower:
+            filters['Rating'] = category_df['Rating'].max()
+    
+    if 'phones' in query_lower:
+        filters['Category'] = 'phone'
+    elif 'laptops' in query_lower:
+        filters['Category'] = 'laptop'
+
+    return filters
+
+def apply_filters(df, filters):
+    """Apply filters to a DataFrame based on the provided filter dictionary."""
+    for key, value in filters.items():
+        if key in df.columns:
+            if isinstance(value, str):
+                df = df[df[key].str.contains(value, case=False, na=False)]
+            elif isinstance(value, (int, float)):
+                df = df[df[key] == value]
+    return df
+
+def fetch_response_from_df(query, category_df, format_response_func):
+    """Fetch response from the dataset based on the category and filters."""
+    filters = extract_filters(query)
+    
+    filtered_df = apply_filters(category_df, filters)
+    
+    if filtered_df.empty:
+        return "Sorry, I couldn't find an answer in our records."
+
+    responses = []
+    for _, row in filtered_df.iterrows():
+        responses.append(format_response_func(row))
+    
+    if responses:
+        return "\n\n".join(responses)
+    
+    return "Sorry, I couldn't find an answer in our records."
+
+def electronics_response(query):
+    """Get response from the electronics dataset."""
+    return fetch_response_from_df(query, electronics_df, format_electronics_response)
+
+def fashion_response(query):
+    """Get response from the fashion dataset."""
+    return fetch_response_from_df(query, fashion_df, format_fashion_response)
+
 def get_response(user_input):
     """Determine the category and fetch the appropriate response."""
     if 'hi' in user_input.lower() or 'hello' in user_input.lower():
@@ -74,18 +143,60 @@ def get_response(user_input):
     elif category == 'fashion':
         response = fashion_response(user_input)
     else:
-        # Use LLM for more complex queries
-        response = query_llm(user_input)
+        response = "Sorry, I couldn't find an answer in our records."
 
     return response
 
-# Streamlit Interface remains the same
+# Streamlit Interface
 def main():
     st.title("Customer Support Chatbot")
 
+    # Custom CSS for chat bubbles
+    st.markdown("""
+    <style>
+    .chat-container {
+        max-width: 800px;
+        margin: 0 auto;
+        padding: 20px;
+    }
+    .chat-bubble {
+        border-radius: 15px;
+        padding: 10px;
+        margin: 5px 0;
+        max-width: 70%;
+        display: inline-block;
+        word-wrap: break-word;
+    }
+    .user-bubble {
+        background-color: #DCF8C6;
+        float: right;
+        text-align: left;
+    }
+    .assistant-bubble {
+        background-color: #FFFFFF;
+        float: left;
+        text-align: left;
+    }
+    .chat-history {
+        max-height: 500px;
+        overflow-y: auto;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
 
+    st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+    
+    st.markdown('<div class="chat-history">', unsafe_allow_html=True)
+    for message in st.session_state.chat_history:
+        if message['role'] == 'user':
+            st.markdown(f'<div class="chat-bubble user-bubble">{message["content"]}</div>', unsafe_allow_html=True)
+        else:
+            st.markdown(f'<div class="chat-bubble assistant-bubble">{message["content"]}</div>', unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+    
     user_input = st.text_input("Type your message here:")
 
     if st.button("Send"):
@@ -93,9 +204,11 @@ def main():
             response_message = get_response(user_input)
             st.session_state.chat_history.append({"role": "user", "content": user_input})
             st.session_state.chat_history.append({"role": "assistant", "content": response_message})
+            
+            # Clear the input after sending
+            st.session_state.user_input = ''
     
-    for message in st.session_state.chat_history:
-        st.markdown(f"{message['role'].capitalize()}: {message['content']}")
+    st.markdown('</div>', unsafe_allow_html=True)
 
 if __name__ == "__main__":
     main()