Update app.py

app.py

@@ -1,64 +1,71 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch\
+import spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-"""
-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}]
+# Load MedScholar model and tokenizer
+model_name = "yasserrmd/MedScholar-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+model.eval()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Chat function (streaming style)
+@spaces.GPU
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    # Prepare the full conversation
+    conversation = [{"role": "system", "content": system_message}]
+    for user_msg, bot_reply in history:
+        if user_msg:
+            conversation.append({"role": "user", "content": user_msg})
+        if bot_reply:
+            conversation.append({"role": "assistant", "content": bot_reply})
+    conversation.append({"role": "user", "content": message})
 
-    messages.append({"role": "user", "content": message})
+    # Convert conversation into prompt string
+    prompt = ""
+    for turn in conversation:
+        if turn["role"] == "system":
+            prompt += f"<|system|>\n{turn['content']}\n"
+        elif turn["role"] == "user":
+            prompt += f"<|user|>\n{turn['content']}\n"
+        elif turn["role"] == "assistant":
+            prompt += f"<|assistant|>\n{turn['content']}\n"
+    prompt += "<|assistant|>\n"
 
-    response = ""
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    # Generate with streaming-like loop
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=max_tokens,
         temperature=temperature,
         top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        do_sample=True,
+        eos_token_id=tokenizer.eos_token_id,
+    )
 
-        response += token
-        yield response
+    # Decode and stream the new content
+    decoded = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    response = decoded.split("<|assistant|>\n")[-1].strip()
+    yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Build Gradio interface
 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)",
-        ),
+        gr.Textbox(value="You are a helpful medical assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=1024, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=1.5, 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"),
     ],
+    title="🩺 MedScholar-1.5B: Medical Chatbot"
 )
 
-
 if __name__ == "__main__":
     demo.launch()