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README.md

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 ---
-title: Med Demo App
-emoji: ⚡
-colorFrom: gray
-colorTo: gray
+title: med_demo_app
+app_file: chatbot_ui.py
 sdk: gradio
 sdk_version: 5.34.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

chatbot_ui.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from termcolor import colored
+
+# --- Model and Tokenizer Loading ---
+MODEL_PATH = "01/medical_model_rl/final"
+TOKENIZER_PATH = "01/medical_model_rl/final"
+
+print("Loading model and tokenizer...")
+
+try:
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, padding_side='left')
+    model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    model.eval()
+
+    print(colored("Model loaded successfully.", "green"))
+
+except Exception as e:
+    print(colored(f"Error loading model: {e}", "red"))
+    model = None
+    tokenizer = None
+
+# --- Chatbot Inference Function ---
+def medical_chatbot(message, history):
+    """
+    Generates a response from the medical chatbot model.
+    """
+    if not model or not tokenizer:
+        return "Error: Model is not loaded. Please check the console for errors."
+
+    try:
+        # Format the prompt
+        full_prompt = f"Question: {message}\n\nAnswer:"
+
+        # Tokenize the input
+        inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True).to(device)
+
+        # Generate a response
+        with torch.no_grad():
+            output_sequences = model.generate(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+                max_length=128,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                num_return_sequences=1,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+        
+        # Decode the response
+        response_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
+        
+        # Extract only the answer part
+        answer = response_text.split("Answer:")[-1].strip()
+
+        return answer
+
+    except Exception as e:
+        print(colored(f"An error occurred during inference: {e}", "red"))
+        return "Sorry, I encountered an error. Please try again."
+
+# --- Gradio UI ---
+chatbot_interface = gr.ChatInterface(
+    fn=medical_chatbot,
+    title="Medical Chatbot",
+    description="Ask any medical question, and the AI will try to answer.",
+    examples=[
+        ["What are the symptoms of diabetes?"],
+        ["How does metformin work?"],
+        ["What is the difference between a virus and a bacteria?"],
+    ],
+    theme="soft",
+).launch(share=True) 

requirements.txt

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+datasets
+tokenizers
+transformers
+torch
+termcolor
+beautifulsoup4
+detoxify
+rouge-score
+tqdm
+huggingface_hub
+trl
+ipywidgets
+tensorboard
+bitsandbytes