Update app.py

app.py

@@ -12,13 +12,13 @@ def load_model():
 # Load the model once
 client = load_model()
 
-# Define prompt templates with a specific name for the assistant
+# Define prompt templates with the assistant's new persona
 def create_prompt(user_message):
     return f"""
-    You are Vishal, a helpful assistant. Respond to the following query as naturally and informatively as possible.
+    You are Katheriya, a skilled data scientist who helps users find the best information from around the globe. You are highly knowledgeable and provide insightful, detailed responses.
 
     User: {user_message}
-    Vishal:
+    Katheriya:
     """
 
 # Function to process the query using the open-source LLM for general chat
@@ -42,7 +42,7 @@ def chat_with_llm(query):
             if 'choices' in message and message['choices']:
                 delta_content = message['choices'][0]['delta'].get('content', '')
                 response_text += delta_content
-                response_container.write(response_text)  # Update response in real-time
+                response_container.write(f"**Katheriya:** {response_text}")  # Update response in real-time
         return response_text
     except Exception as e:
         st.error(f"An error occurred: {e}")
@@ -50,18 +50,24 @@ def chat_with_llm(query):
 # Function to process the query for search intent
 def process_query_with_llm(query):
     prompt = f"User asked: '{query}'. What would be the best search query to use?"
-    response = client.text_generation(prompt, max_length=50, num_return_sequences=1)
-    return response[0]['generated_text'].strip()
+    
+    # Generate response using text_generation without max_length
+    response = client.text_generation(prompt)  # Removed max_length and num_return_sequences
+    return response[0]['generated_text'].strip() if response else "No query generated."
 
 # Function to perform a Google search using the googlesearch-python package
 def search_web(query):
-    search_results = []
-    for result in search(query, num_results=10):
-        search_results.append(result)
-    return search_results
+    try:
+        search_results = []
+        for result in search(query, num_results=10):
+            search_results.append(result)
+        return search_results
+    except Exception as e:
+        st.error(f"An error occurred during web search: {e}")
+        return []
 
 # Streamlit UI
-st.title("Interactive Chatbot")
+st.title("Interactive Chatbot - Powered by Katheriya")
 
 # Input field for user query
 user_input = st.text_input("You:", "")
@@ -95,9 +101,8 @@ if user_input:
             st.write("Sorry, I couldn't find any relevant links.")
     else:
         # Handle general conversation with response streaming
-        st.write("**Chatbot is typing...**")
         response = chat_with_llm(user_input)
-        st.write(f"**Chatbot:** {response}")
+
 
 # import streamlit as st
 # from huggingface_hub import InferenceClient