Create app.py

app.py

@@ -0,0 +1,182 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+import time
+
+# =======================================================
+# Session state to track multi-step questions
+# =======================================================
+session_answers = {}
+
+# =======================================================
+# Load Model
+# =======================================================
+model_name = "augtoma/qCammel-13"
+
+print("Loading tokenizer and model...")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True
+)
+model.eval()
+
+print("Model loaded successfully!")
+print(f"Device map: {model.hf_device_map}")
+print(f"Model device: {next(model.parameters()).device}")
+print(f"GPU Memory: {torch.cuda.memory_allocated()/1024**3:.2f} GB")
+
+# =======================================================
+# Generate Response with token-by-token streaming
+# =======================================================
+def generate_doctor_response(history, session_answers):
+    user_message = history[-1]["content"]
+    
+    if not user_message.strip():
+        history.append({"role": "assistant", "content": "⚠️ Please describe your symptoms or ask a question."})
+        yield history
+        return
+    
+    # Build conversation prompt
+    prompt = """You are an experienced doctor conducting a medical consultation. Your role is to:
+1. Ask one follow-up question at a time
+2. Provide advice or suggestions if possible
+3. Be conversational, caring, and thorough\n\n"""
+    
+    # Include last 5 exchanges
+    recent_history = history[-11:-1] if len(history) > 11 else history[:-1]
+    for msg in recent_history:
+        role = "Patient" if msg["role"] == "user" else "Doctor"
+        content = msg['content'].replace(
+            "⚕️ *Note: This is AI-generated information and not a substitute for professional medical advice. Please consult a healthcare provider for proper diagnosis and treatment.*",
+            ""
+        ).strip()
+        prompt += f"{role}: {content}\n"
+    
+    prompt += f"Patient: {user_message}\nDoctor:"
+    
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device)
+    
+    gen_config = GenerationConfig(
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        max_new_tokens=120,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        repetition_penalty=1.2
+    )
+    
+    input_length = inputs["input_ids"].shape[1]
+    torch.cuda.synchronize() if torch.cuda.is_available() else None
+    
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            generation_config=gen_config
+        )
+    
+    torch.cuda.synchronize() if torch.cuda.is_available() else None
+    
+    # Decode and clean response
+    generated_ids = output_ids[0][input_length:]
+    response = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
+    
+    # Stop at hints of patient message
+    stop_patterns = [
+        "Patient:", "\nPatient", "P:", "How are you", "I am feeling", "Thanks"
+    ]
+    min_stop_pos = len(response)
+    for pattern in stop_patterns:
+        pos = response.lower().find(pattern.lower())
+        if pos != -1 and pos < min_stop_pos:
+            min_stop_pos = pos
+    response = response[:min_stop_pos].strip()
+    
+    if response.lower().startswith("doctor:"):
+        response = response[7:].strip()
+    
+    if len(response) < 10:
+        response = "I understand your concern. Could you please provide more details about your symptoms so I can assist you better?"
+    
+    # Append assistant placeholder for streaming
+    history.append({"role": "assistant", "content": ""})
+    
+    # Stream token by token
+    for i in range(0, len(response), 4):
+        chunk = response[:i+4]
+        history[-1]["content"] = chunk + "▌"
+        yield history.copy()
+        time.sleep(0.015)
+    
+    # Final response with disclaimer
+    history[-1]["content"] = response
+    yield history
+
+# =======================================================
+# Gradio Interface
+# =======================================================
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🩺 AI Doctor Chat Assistant")
+    
+    chatbot = gr.Chatbot(
+        label="💬 Doctor Consultation",
+        type='messages',
+        avatar_images=(
+            "https://cdn-icons-png.flaticon.com/512/706/706830.png",  # Patient
+            "https://cdn-icons-png.flaticon.com/512/3774/3774299.png"   # Doctor
+        ),
+        height=500
+    )
+    
+    with gr.Row():
+        user_input = gr.Textbox(
+            placeholder="Type your symptoms or question here...",
+            label="🧍 Your Message",
+            lines=2,
+            scale=4
+        )
+    
+    with gr.Row():
+        send_btn = gr.Button("💬 Send", variant="primary", scale=1)
+        clear_btn = gr.Button("🧹 Clear Chat", scale=1)
+    
+    gr.Examples(
+        examples=[
+            "I have a fever of 102°F since yesterday",
+            "I've been having headaches for the past week",
+            "I feel very tired all the time",
+            "I have a sore throat and body aches",
+        ],
+        inputs=user_input,
+        label="💡 Example Questions"
+    )
+    
+    # Response function
+    def respond(message, history):
+        global session_answers
+        if history is None:
+            history = []
+        if not message.strip():
+            return "", history
+        history.append({"role": "user", "content": message})
+        for updated_history in generate_doctor_response(history, session_answers):
+            yield "", updated_history
+    
+    # Event handlers
+    send_btn.click(respond, [user_input, chatbot], [user_input, chatbot])
+    user_input.submit(respond, [user_input, chatbot], [user_input, chatbot])
+    clear_btn.click(lambda: [], None, chatbot, queue=False)
+
+# Launch
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(share=True)