Update app.py

app.py

@@ -9,84 +9,17 @@ base_model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B")
 base_model.resize_token_embeddings(len(tokenizer))
 model = PeftModel.from_pretrained(base_model, "BeastGokul/Bio-Mistral-7B-finetuned")
 
-
-
-def generate_response(chat_history, max_length, temperature, top_p):
-    conversation = "\n".join([f"User: {msg[0]}\nModel: {msg[1]}" for msg in chat_history])
-    inputs = tokenizer(conversation, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-    output = model.generate(**inputs, max_length=max_length, temperature=temperature, top_p=top_p, do_sample=True)
-    response_text = tokenizer.decode(output[0], skip_special_tokens=True).split("Model:")[-1].strip()
-    chat_history.append((chat_history[-1][0], response_text))
-    return chat_history, chat_history
-
-with gr.Blocks(css="""
-    .chatbox { max-height: 600px; overflow-y: auto; background-color: #f8f9fa; border: 1px solid #e0e0e0; padding: 10px; border-radius: 8px; }
-    .message { padding: 8px; margin: 4px 0; border-radius: 6px; }
-    .user-message { background-color: #cce5ff; text-align: left; }
-    .model-message { background-color: #e2e3e5; text-align: left; }
-""") as interface:
-    gr.Markdown(
-        """
-        <h1 style="text-align:center; color: #2c3e50;">Biomedical AI Chat Interface</h1>
-        <p style="text-align:center; color: #34495e;">
-            Ask any biomedical or health-related questions to interact with the AI.
-        </p>
-        """
-    )
-
-    chat_history = gr.State([])
-
-    with gr.Row():
-        user_input = gr.Textbox(
-            lines=2,
-            placeholder="Type your biomedical query here...",
-            label="Your Message",
-            elem_id="user-input",
-            container=False
-        )
-        chat_display = gr.Chatbot(label="Chat History", elem_id="chatbox", elem_classes=["chatbox"])
-
-
-    example_queries = [
-        "What are the common symptoms of diabetes?",
-        "Explain the function of hemoglobin.",
-        "How does insulin work in the body?",
-        "What are the side effects of chemotherapy?",
-        "Can you explain the process of DNA replication?"
-    ]
+def generate_response(user_query):
+    inputs = tokenizer(user_query, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=100)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
     user_input = gr.Textbox(placeholder="Enter your biomedical query...", label="Your Query")
+    response = gr.Textbox(label="Response", interactive=False)
+    
+    user_input.submit(fn=generate_response, inputs=user_input, outputs=response)
 
-
-    with gr.Row():
-        max_length = gr.Slider(50, 500, value=200, step=10, label="Response Length")
-        temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
-        top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="Top-p")
-
-    send_button = gr.Button("Send", elem_id="send-button")
-
-    with gr.Row():
-        for query in example_queries:
-            gr.Button(query).click(fn=lambda q=query: (q, []), outputs=[user_input, chat_history])
-
-    def add_user_message(user_message, chat_history):
-        chat_history.append((user_message, ""))
-        return "", chat_history
-
-    send_button.click(
-        fn=add_user_message,
-        inputs=[user_input, chat_history],
-        outputs=[user_input, chat_history],
-    )
-
-    send_button.click(
-        fn=generate_response,
-        inputs=[chat_history, max_length, temperature, top_p],
-        outputs=[chat_display, chat_history],
-    )
-
-interface.launch()
-
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()