Update app.py

app.py

@@ -1,64 +1,38 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Model name (use a specialized medical model for better results)
+model_name = "microsoft/DialoGPT-medium"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Initialize the pipeline
+chatbot_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def generate_response(user_input):
+    # Generate a response using the model
+    responses = chatbot_pipeline(user_input, max_length=150, num_return_sequences=1)
+    response = responses[0]['generated_text']
+    
+    # Optional: Post-process the response
+    return response.strip()
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Ask a medical question..."),
+    outputs="text",
+    title="AI Patient Interaction Chatbot",
+    description="Ask any health-related questions and get real-time answers.",
+    examples=[
+        ["What are the symptoms of diabetes?"],
+        ["How can I manage my hypertension?"],
+        ["What should I do if I have a headache?"],
     ],
+    theme="compact"
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the interface
+iface.launch()