Update pages/chatbot.py

pages/chatbot.py

@@ -1,86 +1,73 @@
 import streamlit as st
-import pandas as pd
+import requests
 
-# Load dataset
-data = pd.read_csv("Cleaned_Dataset.csv")
+# Set up Gemini API configuration
+GEMINI_API_KEY = "AIzaSyDnQfeYA4fQ-gFUyY16611nZv7rvdQ9Ii0"
+GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent"
 
-# Chatbot functions
-def parse_user_input(user_input):
-    intents = {
-        "time_spent": ["how much time", "time spent"],
-        "addiction_level": ["addicted", "addiction level"],
-        "reduce_addiction": ["reduce addiction", "how to stop"]
+# Function to call Gemini API
+def query_gemini(prompt):
+    headers = {
+        "Content-Type": "application/json"
     }
+    data = {
+        "contents": [{
+            "parts": [{"text": prompt}]
+        }]
+    }
+    params = {
+        "key": GEMINI_API_KEY
+    }
+    response = requests.post(GEMINI_API_URL, headers=headers, json=data, params=params)
     
-    intent = None
-    for key, keywords in intents.items():
-        if any(keyword in user_input.lower() for keyword in keywords):
-            intent = key
-            break
-    
-    platforms = ["tiktok", "instagram", "facebook", "youtube"]
-    platform = None
-    for p in platforms:
-        if p in user_input.lower():
-            platform = p.capitalize()
-            break
-    
-    return intent, platform
-
-def generate_response(intent, platform, data):
-    if intent == "time_spent":
-        if platform:
-            filtered_data = data[data["Platform"] == platform]
-            if not filtered_data.empty:
-                avg_time_spent = filtered_data["Total Time Spent"].mean()
-                return f"The average time spent on {platform} is {avg_time_spent:.2f} minutes."
-            else:
-                return f"No data available for {platform}."
-        else:
-            return "Please specify a platform (e.g., TikTok, Instagram)."
+    if response.status_code == 200:
+        # Extract the response text from the JSON
+        response_data = response.json()
+        try:
+            return response_data['candidates'][0]['content']['parts'][0]['text']
+        except KeyError:
+            return "Sorry, I couldn't process that request."
+    else:
+        return f"Error: {response.status_code}, {response.text}"
 
-    elif intent == "addiction_level":
-        if platform:
-            filtered_data = data[data["Platform"] == platform]
-            if not filtered_data.empty:
-                avg_addiction = filtered_data["Addiction Level"].mean()
-                return f"The average addiction level for {platform} users is {avg_addiction:.2f}."
-            else:
-                return f"No data available for {platform}."
-        else:
-            return "Please specify a platform (e.g., TikTok, Instagram)."
+# Streamlit App
+def main():
+    st.set_page_config(page_title="Gemini Chatbot", page_icon="🤖")
+    st.title("🤖 Gemini-Powered Chatbot")
+    st.markdown("Ask me anything! I'll do my best to help.")
 
-    elif intent == "reduce_addiction":
-        return (
-            "Here are some tips to reduce social media addiction:\n"
-            "1. Set daily screen time limits.\n"
-            "2. Take regular breaks from social media.\n"
-            "3. Identify triggers (e.g., boredom, procrastination) and find alternative activities.\n"
-            "4. Use apps to monitor and limit your usage."
-        )
+    # Initialize session state for chat history
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
 
-    else:
-        return "Sorry, I couldn't understand your query. Please try rephrasing."
+    # Display chat history
+    for message in st.session_state.chat_history:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
 
-# Streamlit app
-def chatbot_page():
-    st.title("Social Media Addiction Chatbot")
-    st.write("Ask questions about your social media usage or addiction!")
-    
-    # Chat interface
-    user_input = st.chat_input("Ask a question...")
+    # User input
+    user_input = st.chat_input("Type your question here...")
     if user_input:
-        st.write(f"User input received: {user_input}")
-        
-        # Parse user input
-        intent, platform = parse_user_input(user_input)
+        # Add user message to chat history
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
         
-        # Generate response
-        response = generate_response(intent, platform, data)
-        st.write(response)
+        # Display user message
+        with st.chat_message("user"):
+            st.markdown(user_input)
+
+        # Query Gemini API
+        with st.spinner("Thinking..."):
+            gemini_response = query_gemini(user_input)
+
+        # Add assistant message to chat history
+        st.session_state.chat_history.append({"role": "assistant", "content": gemini_response})
+
+        # Display assistant message
+        with st.chat_message("assistant"):
+            st.markdown(gemini_response)
 
 if __name__ == "__main__":
-    chatbot_page()
+    main()
 # import streamlit as st
 # import pandas as pd
 # import google.generativeai as genai