Update app.py

app.py

@@ -6,48 +6,66 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Load the model and tokenizer manually
+
 model_name = "BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Create a pipeline using the manually loaded model and tokenizer
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
 
 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 if msg[1]])
-    input_text = f"{conversation}\nUser: {chat_history[-1][0]}\nModel:"
-    
-    # Use the pipeline to generate a response
-    generated_text = pipe(
-        input_text,
-        max_length=max_length,
-        temperature=temperature,
-        top_p=top_p,
-        num_return_sequences=1
-    )[0]["generated_text"]
-    
-    # Extract only the model's response after "Model:"
-    response_text = generated_text.split("Model:")[-1].strip()
-    chat_history[-1] = (chat_history[-1][0], response_text)
+    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() as interface:
-    gr.Markdown("# Biomedical AI Chat Interface")
-    
+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(placeholder="Enter your biomedical query...")
-        chat_display = gr.Chatbox(label="Chat History")
+        user_input = gr.Textbox(
+            lines=2,
+            placeholder="Type your biomedical query here...",
+            label="Your Message",
+            elem_id="user-input",
+            container=False
+        )
+        chat_display = gr.Chatbox(label="Chat History", elem_id="chatbox", css_class="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?"
+    ]
+    user_input.style(placeholder="Enter your biomedical query...")
 
     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")
+    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, ""))
@@ -68,5 +86,6 @@ with gr.Blocks() as interface:
 interface.launch()
 
 
+
 if __name__ == "__main__":
     demo.launch()