Update app.py

app.py

@@ -1,73 +1,106 @@
 import streamlit as st
-import google.generativeai as genai  
-import sqlite3
+import google.generativeai as genai
+import sqlite3 
+from streamlit import file_uploader
 
 # Database setup
-conn = sqlite3.connect('chat_history.db')   
+conn = sqlite3.connect('chat_history.db')  
 c = conn.cursor()
 
 c.execute('''
-          CREATE TABLE IF NOT EXISTS history
-          (role TEXT, message TEXT)  
+          CREATE TABLE IF NOT EXISTS history  
+          (role TEXT, message TEXT)
           ''')
 
-# API setup 
-api_key = "AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM"
-genai.configure(api_key=api_key)   
+# Generative AI setup
+api_key = "AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM"   
+genai.configure(api_key=api_key)
 
 generation_config = {
   "temperature": 0.9,
-  "max_output_tokens": 3000 
+  "max_output_tokens": 3000  
 }
 
+safety_settings = []
+
 # Streamlit UI
-st.title("Chatbot")   
+st.title("Chatbot")
 
-if "chat_history" not in st.session_state:
-    st.session_state["chat_history"] = []
-    
-chat_history = st.session_state["chat_history"]
+chat_history = st.session_state.get("chat_history", []) 
+
+if len(chat_history) % 2 == 0:
+    role = "user"
+else:
+    role = "model"
 
 for message in chat_history:
     r, t = message["role"], message["parts"][0]["text"]
     st.markdown(f"**{r.title()}:** {t}")
     
-user_input = st.text_input("You: ")  
-
-# Image upload
-uploaded_files = st.file_uploader("Upload Images", type=["png","jpg","jpeg"], accept_multiple_files=True)   
-image_parts = []
-
-if uploaded_files:
-    for file in uploaded_files:
-        image_parts.append({"mime_type": file.type, "data": file.read()})
-        
+# Use text_area for multiline input   
+user_input = st.text_area("", height=5)  
 if user_input:
-
-    # Append user input
-    chat_history.append({"role": "user", "parts": [{"text": user_input}]})
-    
-    if len(image_parts) > 0:
-        prompt_parts = [user_input] + image_parts
+    chat_history.append({"role": role, "parts": [{"text": user_input}]}) 
+    if role == "user":
+        
+        # Model code
+        model_name = "gemini-pro"
+        model = genai.GenerativeModel(
+            model_name=model_name,   
+            generation_config=generation_config,
+            safety_settings=safety_settings
+        )
         
-        model = genai.GenerativeModel('gemini-pro-vision')
-        response = model.generate_content(prompt_parts, config=generation_config)
+        response = model.generate_content(chat_history) 
+        response_text = response.text
+        chat_history.append({"role": "model", "parts": [{"text": response_text}]})
         
-    else:
-        model = genai.GenerativeModel('gemini-pro')
-        response = model.generate_content(chat_history, config=generation_config)
+        st.session_state["chat_history"] = chat_history
         
-    response_text = response['text'] 
+for message in chat_history:
+    r, t = message["role"], message["parts"][0]["text"]
+    st.markdown(f"**{r.title()}:** {t}")
+if st.button("Display History"): 
+    c.execute("SELECT * FROM history") 
+    rows = c.fetchall() 
 
-    # Append model response
-    chat_history.append({"role": "assistant", "parts": [{"text": response_text}]})
-    
-    st.session_state["chat_history"] = chat_history
-    
+    for row in rows: 
+        st.markdown(f"**{row[0].title()}:** {row[1]}") 
+        
 # Save chat history to database
-for message in chat_history:
+for message in chat_history: 
     c.execute("INSERT INTO history VALUES (?, ?)", 
-              (message["role"], message["parts"][0]["text"])) 
-              
-conn.commit()
-conn.close()
+            (message["role"], message["parts"][0]["text"])) 
+    conn.commit() 
+
+conn.close()
+
+# Separate section for image uploading
+st.title("Image Description Generator")
+
+uploaded_file = st.file_uploader("Upload an image here", type=["png", "jpg", "jpeg"])
+
+# Text input for asking questions about the image
+image_question = st.text_input("Ask something about the image:")
+
+if uploaded_file and image_question:
+   image_parts = [
+     {
+       "mime_type": uploaded_file.type,
+       "data": uploaded_file.read() 
+     },
+   ]
+
+   prompt_parts = [
+     image_question,
+     image_parts[0],
+   ]
+
+   model = genai.GenerativeModel(
+     model_name="gemini-pro-vision", 
+     generation_config=generation_config, 
+     safety_settings=safety_settings 
+   )
+   
+   response = model.generate_content(prompt_parts)
+   st.markdown(f"**Model's answer:** {response.text}")