Update app.py

app.py

@@ -1,29 +1,126 @@
+# import streamlit as st
+# from transformers import pipeline
+# from googlesearch import search
+
+# # Function to load the model
+# @st.cache_resource
+# def load_model():
+#     model_name = "nisten/Biggie-SmoLlm-0.15B-Base"  # Replace with your preferred model
+#     generator = pipeline("text-generation", model=model_name)
+#     return generator
+
+# # Load the model once
+# generator = load_model()
+
+# # Function to process the query using the open-source LLM for general chat
+# def chat_with_llm(query):
+#     prompt = f"User: {query}\nAssistant:"
+#     response = generator(prompt, max_length=100, num_return_sequences=1)
+#     return response[0]['generated_text'].strip()
+
+# # 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 = generator(prompt, max_length=50, num_return_sequences=1)
+#     st.write("query passing for search",response[0]['generated_text'].strip())
+#     return response[0]['generated_text'].strip()
+
+# # 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
+
+# # Streamlit UI
+# st.title("Interactive Chatbot")
+
+# # Input field for user query
+# user_input = st.text_input("You:", "")
+
+# if user_input:
+#     st.write(f"**You:** {user_input}")
+#     search_phrases = [
+#     "search",
+#     "find",
+#     "get me",
+#     "give me",
+#     "look up",
+#     "show me",
+#     "retrieve",
+#     "browse",
+#     "where can I find",
+#     "search for",
+#     "look for",
+#     "can you find",
+#     "find me",
+#     "what is",
+#     "how to",
+#     "who is",
+#     "where is",
+#     "what are",
+#     "tell me about",
+#     "do you know",
+#     "could you find",
+#     "can you search",
+#     "help me find",
+#     "explore",
+#     "fetch",
+#     "locate",
+#     "suggest me",
+#     "suggest"]
+
+#     # Determine if the query is a search or a general chat
+#     if any(keyword in user_input.lower() for keyword in search_phrases):
+#         # If the user input indicates a search intent
+#         search_query = process_query_with_llm(user_input)
+#         st.write(f"**Processed Query:** {search_query}")
+
+#         # Search the web using the processed query
+#         links = search_web(search_query)
+
+#         # Display the search results
+#         if links:
+#             st.write("Here are some links you might find useful:")
+#             for idx, link in enumerate(links):
+#                 st.write(f"{idx + 1}. {link}")
+#         else:
+#             st.write("Sorry, I couldn't find any relevant links.")
+#     else:
+#         # Handle general conversation
+#         response = chat_with_llm(user_input)
+#         st.write(f"**Chatbot:** {response}")
 import streamlit as st
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 from googlesearch import search
 
-# Function to load the model
-@st.cache_resource
-def load_model():
-    model_name = "nisten/Biggie-SmoLlm-0.15B-Base"  # Replace with your preferred model
-    generator = pipeline("text-generation", model=model_name)
-    return generator
+# Initialize the InferenceClient with the model and token only once
+if 'client' not in st.session_state:
+    st.session_state.client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
 
-# Load the model once
-generator = load_model()
+client = st.session_state.client
 
-# Function to process the query using the open-source LLM for general chat
-def chat_with_llm(query):
-    prompt = f"User: {query}\nAssistant:"
-    response = generator(prompt, max_length=100, num_return_sequences=1)
-    return response[0]['generated_text'].strip()
+# Function to get chat completion from the model
+def chat_with_llm(messages):
+    try:
+        response_stream = client.chat_completion(messages=messages, stream=True, max_tokens=500)
+        delta_content = ""
+        
+        for message in response_stream:
+            if 'choices' in message and message['choices']:
+                delta_content += message['choices'][0]['delta'].get('content', '')
+        
+        return delta_content.strip()
+    
+    except Exception as e:
+        return f"An error occurred: {e}"
 
 # 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 = generator(prompt, max_length=50, num_return_sequences=1)
-    st.write("query passing for search",response[0]['generated_text'].strip())
-    return response[0]['generated_text'].strip()
+    messages = [{"role": "user", "content": f"User asked: '{query}'. What would be the best search query to use?"}]
+    response = chat_with_llm(messages)
+    st.write("Query passing for search:", response)
+    return response
 
 # Function to perform a Google search using the googlesearch-python package
 def search_web(query):
@@ -35,40 +132,27 @@ def search_web(query):
 # Streamlit UI
 st.title("Interactive Chatbot")
 
+# Initialize message history if not already done
+if 'message_history' not in st.session_state:
+    st.session_state.message_history = []
+
 # Input field for user query
 user_input = st.text_input("You:", "")
 
+# Check if the input field is not empty
 if user_input:
     st.write(f"**You:** {user_input}")
+
     search_phrases = [
-    "search",
-    "find",
-    "get me",
-    "give me",
-    "look up",
-    "show me",
-    "retrieve",
-    "browse",
-    "where can I find",
-    "search for",
-    "look for",
-    "can you find",
-    "find me",
-    "what is",
-    "how to",
-    "who is",
-    "where is",
-    "what are",
-    "tell me about",
-    "do you know",
-    "could you find",
-    "can you search",
-    "help me find",
-    "explore",
-    "fetch",
-    "locate",
-    "suggest me",
-    "suggest"]
+        "search", "find", "get me", "give me", "look up", "show me", "retrieve",
+        "browse", "where can I find", "search for", "look for", "can you find",
+        "find me", "what is", "how to", "who is", "where is", "what are",
+        "tell me about", "do you know", "could you find", "can you search",
+        "help me find", "explore", "fetch", "locate", "suggest me", "suggest"
+    ]
+
+    # Update message history with user input
+    st.session_state.message_history.append({"role": "user", "content": user_input})
 
     # Determine if the query is a search or a general chat
     if any(keyword in user_input.lower() for keyword in search_phrases):
@@ -88,5 +172,12 @@ if user_input:
             st.write("Sorry, I couldn't find any relevant links.")
     else:
         # Handle general conversation
-        response = chat_with_llm(user_input)
+        response = chat_with_llm(st.session_state.message_history)
         st.write(f"**Chatbot:** {response}")
+
+        # Update message history with the response
+        st.session_state.message_history.append({"role": "assistant", "content": response})
+
+    # Ensure input field is cleared after processing
+    st.text_input("You:", "", key="user_input")
+