Update app.py

app.py

@@ -84,38 +84,86 @@ def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
         return f"Error: {str(e)}"
 
 # Gradio Interface with Chatbot
-with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")) as chatbot_app:
-    gr.HTML("""
-    <center>
-        <h1><span style="color: purple;">Science Chatbot for 10th Grade Students</span></h1>
-        <h2>AI-powered Science Tutor</h2>
-        <i>⚠️ Ask any question from 10th-grade science (physics, chemistry, biology) and get clear, accurate answers! ⚠️</i>
-    </center>
-    """)
-    
-    chatbot = gr.Chatbot(label="Conversation")
+with gr.Blocks(title="Science Chatbot", theme='gradio/soft') as chatbot_app:
+    history_state = gr.State([])
     with gr.Row():
-        query = gr.Textbox(label="Ask a Science Question", placeholder="E.g., What is an ionic bond?", show_label=False)
-        submit_btn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=10):
+            gr.HTML("""
+            <div style="color: #FF4500;">
+                <h1>Welcome! I am your friend!</h1>
+                <h1>Ask me! I will help you</h1>
+                <h1><span style="color: #008000">I AM A CHATBOT FOR 10TH SCIENCE</span></h1>
+            </div>
+            <p style="font-family: sans-serif; font-size: 16px;">
+                A free chatbot developed by K.M.RAMYASRI, TGT, GHS.SUTHUKENY using Phi Agent & Groq LLMs for 10th-grade students
+            </p>
+            <p style="font-family: Arial, sans-serif; font-size: 14px;">
+                Suggestions may be sent to <a href="mailto:ramyasriraman2019@gmail.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.
+            </p>
+            """)
+        with gr.Column(scale=3):
+            gr.Image(value='logo.png', height=200, width=200, show_label=False)
     
-    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings")
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        avatar_images=(
+            'https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
+            'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'
+        ),
+        bubble_full_width=False,
+        show_copy_button=True,
+        show_share_button=True,
+        label="Conversation"
+    )
+    
+    with gr.Row():
+        query = gr.Textbox(
+            scale=3,
+            show_label=False,
+            placeholder="Enter text and press enter",
+            container=False
+        )
+        submit_btn = gr.Button(value="Submit", scale=1, variant="primary")
+    
+    cross_encoder = gr.Radio(
+        choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'],
+        value='(ACCURATE) BGE reranker',
+        label="Embeddings",
+        info="Select the model for document ranking"
+    )
     
-    def handle_query(user_input, history, cross_encoder_choice):
-        if not user_input.strip():
-            return history, ""
-        history = history or []
-        response = retrieve_and_generate_response(user_input, cross_encoder_choice, history)
-        history.append([user_input, response])
+    def handle_query(txt, cross_encoder, history_state):
+        if not txt.strip():
+            gr.Warning("Please submit a non-empty question")
+            return history_state, ""
+        history = history_state or []
+        history.append((txt, ""))
+        response = retrieve_and_generate_response(txt, cross_encoder, history[:-1])
+        history[-1] = (txt, response)
+        history_state[:] = history
         return history, ""
     
     submit_btn.click(
         fn=handle_query,
-        inputs=[query, chatbot, cross_encoder],
+        inputs=[query, cross_encoder, history_state],
+        outputs=[chatbot, query]
+    )
+    query.submit(
+        fn=handle_query,
+        inputs=[query, cross_encoder, history_state],
         outputs=[chatbot, query]
     )
+    
+    examples = [
+        'CAN U SAY THE DIFFERENCES BETWEEN METALS AND NON METALS?',
+        'WHAT IS IONIC BOND?',
+        'EXPLAIN ASEXUAL REPRODUCTION'
+    ]
+    gr.Examples(examples, query)
 
 if __name__ == "__main__":
-    chatbot_app.queue().launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
+    chatbot_app.launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
 # import logging
 # from sentence_transformers import CrossEncoder
 # from phi.agent import Agent
@@ -200,6 +248,123 @@ if __name__ == "__main__":
 #         logger.error(f"Error in response generation: {e}")
 #         return f"Error: {str(e)}"
 
+# # Gradio Interface with Chatbot
+# with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")) as chatbot_app:
+#     gr.HTML("""
+#     <center>
+#         <h1><span style="color: purple;">Science Chatbot for 10th Grade Students</span></h1>
+#         <h2>AI-powered Science Tutor</h2>
+#         <i>⚠️ Ask any question from 10th-grade science (physics, chemistry, biology) and get clear, accurate answers! ⚠️</i>
+#     </center>
+#     """)
+    
+#     chatbot = gr.Chatbot(label="Conversation")
+#     with gr.Row():
+#         query = gr.Textbox(label="Ask a Science Question", placeholder="E.g., What is an ionic bond?", show_label=False)
+#         submit_btn = gr.Button("Submit", variant="primary")
+    
+#     cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings")
+    
+#     def handle_query(user_input, history, cross_encoder_choice):
+#         if not user_input.strip():
+#             return history, ""
+#         history = history or []
+#         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history)
+#         history.append([user_input, response])
+#         return history, ""
+    
+#     submit_btn.click(
+#         fn=handle_query,
+#         inputs=[query, chatbot, cross_encoder],
+#         outputs=[chatbot, query]
+#     )
+
+# if __name__ == "__main__":
+#     chatbot_app.queue().launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
+# # import logging
+# from sentence_transformers import CrossEncoder
+# from phi.agent import Agent
+# from phi.model.groq import Groq
+# from backend.semantic_search import table, retriever
+# import numpy as np
+# from time import perf_counter
+# import os
+
+# # Logging setup
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# # API Key setup
+# api_key = os.getenv("GROQ_API_KEY")
+# if not api_key:
+#     logger.error("GROQ_API_KEY not found.")
+#     gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
+# else:
+#     os.environ["GROQ_API_KEY"] = api_key
+
+# # Initialize Phi Agent
+# def create_phi_agent():
+#     if not api_key:
+#         return None
+#     agent = Agent(
+#         name="Science Education Assistant",
+#         role="You are a helpful science tutor for 10th-grade students",
+#         instructions=[
+#             "You are an expert science teacher specializing in 10th-grade curriculum.",
+#             "Provide clear, accurate, and age-appropriate explanations.",
+#             "Use simple language and examples that students can understand.",
+#             "Focus on concepts from physics, chemistry, and biology.",
+#             "Structure responses with headings and bullet points when helpful.",
+#             "Encourage learning and curiosity."
+#         ],
+#         model=Groq(id="llama3-70b-8192", api_key=api_key),
+#         markdown=True
+#     )
+#     return agent
+
+# phi_agent = create_phi_agent()
+
+# # Response Generation
+# def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
+#     top_rerank = 25
+#     top_k_rank = 20
+    
+#     if not query:
+#         return "Please provide a valid question."
+    
+#     try:
+#         start_time = perf_counter()
+#         query_vec = retriever.encode(query)
+#         documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list()
+#         documents = [doc["text"] for doc in documents]
+        
+#         cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base') if cross_encoder_choice == '(ACCURATE) BGE reranker' else CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+#         query_doc_pair = [[query, doc] for doc in documents]
+#         cross_scores = cross_encoder1.predict(query_doc_pair)
+#         sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
+#         documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
+        
+#         context = "\n\n".join(documents[:10]) if documents else ""
+#         context = f"Context information from educational materials:\n{context}\n\n"
+        
+#         history_context = ""
+#         if history:
+#             for user_msg, bot_msg in history[-2:]:
+#                 if user_msg and bot_msg:
+#                     history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n\n"
+        
+#         full_prompt = f"{history_context}{context}Question: {query}\n\nPlease answer the question using the context provided above. If the context doesn't contain relevant information, use your general knowledge about 10th-grade science topics."
+        
+#         if not phi_agent:
+#             return "Chatbot not configured properly."
+#         response = phi_agent.run(full_prompt)
+#         logger.info(f"Response generation took {perf_counter() - start_time:.2f} seconds")
+#         return response.content if hasattr(response, 'content') else str(response)
+    
+#     except Exception as e:
+#         logger.error(f"Error in response generation: {e}")
+#         return f"Error: {str(e)}"
+
 # # Gradio Interface (Inspired by Quiz App)
 # with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")) as chatbot_app:
 #     gr.HTML("""