Update src/app.py

src/app.py

@@ -1,130 +1,129 @@
-import streamlit as st
-from run_model import generate_response, generate_RAG_response
-from web_search import search_web
-from deep_research import perform_deep_research
-import tempfile
-
-st.set_page_config(layout="wide")
-
-def main():
-    st.title("💬 Chat with Gemma")
-    
-
-    with st.sidebar:
-        st.title("Tools")
-        st.markdown("For source code of this website, visit : <some_link>")
-        
-        option = st.selectbox(
-            "Choose tools",
-            ("Simple Chat", "Web Search", "Upload PDF", "Deep Web Search"),
-        )
-
-        temperature = st.slider(
-            label="Temperature (controls randomness)",
-            min_value=0.0,
-            max_value=2.0,
-            value=1.0,
-            step=0.01,
-            help="Lower = more deterministic, Higher = more random"
-        )
-        # Top-k sampling
-        top_k = st.slider(
-            label="Top-k (limits to top K tokens by probability)",
-            min_value=0,
-            max_value=100,
-            value=50,
-            step=1,
-            help="0 = disable top-k filtering"
-        )
-
-        # Top-p (nucleus sampling)
-        top_p = st.slider(
-            label="Top-p (nucleus sampling cutoff)",
-            min_value=0.0,
-            max_value=1.0,
-            value=0.9,
-            step=0.01,
-            help="0.0 = conservative, 1.0 = more random"
-        )
-
-        if option == "Upload PDF":
-            file = st.file_uploader(label="Uploaded file will provide context to LLM", type="pdf")
-            
-
-        if option == "Web Search":
-            st.write("Web Search Enabled for next query")
-            # WB_SEARCH = True
-
-        if option == "None":
-            st.warning("You are not using any tool")
-
-        if option == "Deep Web Search":
-            st.write("Deep Web Research Enabled for next query")
-
-
-
-    # col1, col2 = st.columns([6, 1], gap="small")
-
-    # column 1
-    # with col1:
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    
-    for msg in st.session_state.messages:
-        with st.chat_message(msg["role"]):
-            st.markdown(msg["content"])
-
-    # prompt = st.chat_input("Say something...")
-
-    if prompt:= st.chat_input("Say something..."):
-        # Display user message
-        st.chat_message("user").markdown(prompt)
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        if option == "Simple Chat":
-
-            response = generate_response(history=st.session_state.messages, query=prompt, temperature=temperature, top_k=top_k, top_p=top_p)
-            st.chat_message("assistant").markdown(response)
-            # st.session_state.messages.append({"role": "assistant", "content": response})
-        
-        if option == "Web Search":
-            st.session_state.messages.append([{"role": "user", "content" : prompt}])
-
-            response, sources = search_web(prompt)
-            # asnswer = response
-            # st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
-            with st.chat_message("assistant"):
-                st.markdown(f"{response}\n\n### Sources\n" + "\n".join(sources))
-
-        if option == "Deep Web Search":
-            st.session_state.messages.append([{"role": "user", "content" : prompt}])
-
-            response = perform_deep_research(prompt)
-            # asnswer = response
-            # st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
-            with st.chat_message("assistant"):
-                st.markdown(response)
-
-        if option == "Upload PDF":
-            st.session_state.messages.append([{"role": "user", "content" : prompt}])
-
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
-                tmp_file.write(file.read())
-                tmp_path = tmp_file.name
-            print(tmp_path)
-            response = generate_RAG_response(prompt, tmp_path, st.session_state.messages)
-            # print(file)
-            with st.chat_message("assistant"):
-                st.markdown(response)
-        
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        
-        
-    # # Slider in column 2
-    # with col2:
-    #     temperature = st.slider("Temperature", 0.0, 1.0, 0.0)
-    #     top_k = st.slider("Top k", 0.0, 100.0, 40.0)
-    #     top_p = st.slider("Top p", 0.0, 1.0, 0.95)
-
-
-if __name__ == "__main__":
+import streamlit as st
+from run_model import generate_response, generate_RAG_response
+from web_search import search_web
+from deep_research import perform_deep_research
+import tempfile
+
+st.set_page_config(layout="wide")
+
+def main():
+    st.title("Chat with AI")
+    
+
+    with st.sidebar:
+        st.title("Tools")
+        
+        option = st.selectbox(
+            "Choose tools",
+            ("Simple Chat", "Web Search", "Upload PDF", "Deep Web Search"),
+        )
+
+        temperature = st.slider(
+            label="Temperature (controls randomness)",
+            min_value=0.0,
+            max_value=2.0,
+            value=1.0,
+            step=0.01,
+            help="Lower = more deterministic, Higher = more random"
+        )
+        # Top-k sampling
+        top_k = st.slider(
+            label="Top-k (limits to top K tokens by probability)",
+            min_value=0,
+            max_value=100,
+            value=50,
+            step=1,
+            help="0 = disable top-k filtering"
+        )
+
+        # Top-p (nucleus sampling)
+        top_p = st.slider(
+            label="Top-p (nucleus sampling cutoff)",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.9,
+            step=0.01,
+            help="0.0 = conservative, 1.0 = more random"
+        )
+
+        if option == "Upload PDF":
+            file = st.file_uploader(label="Uploaded file will provide context to LLM", type="pdf")
+            
+
+        if option == "Web Search":
+            st.write("Web Search Enabled for next query")
+            # WB_SEARCH = True
+
+        if option == "None":
+            st.warning("You are not using any tool")
+
+        if option == "Deep Web Search":
+            st.write("Deep Web Research Enabled for next query")
+
+
+
+    # col1, col2 = st.columns([6, 1], gap="small")
+
+    # column 1
+    # with col1:
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    for msg in st.session_state.messages:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
+
+    # prompt = st.chat_input("Say something...")
+
+    if prompt:= st.chat_input("Say something..."):
+        # Display user message
+        st.chat_message("user").markdown(prompt)
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        if option == "Simple Chat":
+
+            response = generate_response(history=st.session_state.messages, query=prompt, temperature=temperature, top_k=top_k, top_p=top_p)
+            st.chat_message("assistant").markdown(response)
+            # st.session_state.messages.append({"role": "assistant", "content": response})
+        
+        if option == "Web Search":
+            st.session_state.messages.append([{"role": "user", "content" : prompt}])
+
+            response, sources = search_web(prompt)
+            # asnswer = response
+            # st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
+            with st.chat_message("assistant"):
+                st.markdown(f"{response}\n\n### Sources\n" + "\n".join(sources))
+
+        if option == "Deep Web Search":
+            st.session_state.messages.append([{"role": "user", "content" : prompt}])
+
+            response = perform_deep_research(prompt)
+            # asnswer = response
+            # st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
+            with st.chat_message("assistant"):
+                st.markdown(response)
+
+        if option == "Upload PDF":
+            st.session_state.messages.append([{"role": "user", "content" : prompt}])
+
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+                tmp_file.write(file.read())
+                tmp_path = tmp_file.name
+            print(tmp_path)
+            response = generate_RAG_response(prompt, tmp_path, st.session_state.messages)
+            # print(file)
+            with st.chat_message("assistant"):
+                st.markdown(response)
+        
+        st.session_state.messages.append({"role": "assistant", "content": response})
+        
+        
+    # # Slider in column 2
+    # with col2:
+    #     temperature = st.slider("Temperature", 0.0, 1.0, 0.0)
+    #     top_k = st.slider("Top k", 0.0, 100.0, 40.0)
+    #     top_p = st.slider("Top p", 0.0, 1.0, 0.95)
+
+
+if __name__ == "__main__":
     main()