Rename frontend.py to app.py

frontend.py → app.py

@@ -1,313 +1,313 @@
-import os
-import gc
-import torch
-import gradio as gr
-from transformers import LlamaTokenizer, LlamaForCausalLM, StoppingCriteria, StoppingCriteriaList
-
-# =============================
-# Configuration
-# =============================
-MODEL_PATH = r"C:\Users\JAY\Downloads\Chatdoc\ChatDoctor\pretrained"
-MAX_NEW_TOKENS = 200
-TEMPERATURE = 0.5
-TOP_K = 50
-REPETITION_PENALTY = 1.1
-
-# Detect device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Loading model from {MODEL_PATH} on {device}...")
-
-# =============================
-# Load Tokenizer and Model
-# =============================
-tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
-model = LlamaForCausalLM.from_pretrained(
-    MODEL_PATH,
-    device_map="auto",
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True
-)
-
-generator = model.generate
-print("✅ ChatDoctor model loaded successfully!\n")
-
-# =============================
-# Stopping Criteria
-# =============================
-class StopOnTokens(StoppingCriteria):
-    def __init__(self, stop_ids):
-        self.stop_ids = stop_ids
-
-    def __call__(self, input_ids, scores, **kwargs):
-        for stop_id_seq in self.stop_ids:
-            if len(stop_id_seq) == 1:
-                if input_ids[0][-1] == stop_id_seq[0]:
-                    return True
-            else:
-                if len(input_ids[0]) >= len(stop_id_seq):
-                    if input_ids[0][-len(stop_id_seq):].tolist() == stop_id_seq:
-                        return True
-        return False
-
-# =============================
-# Chat History (Global)
-# =============================
-conversation_history = []
-
-# =============================
-# Get Response Function
-# =============================
-def get_response(user_input, history_context):
-    """Generate response from ChatDoctor model"""
-    human_invitation = "Patient: "
-    doctor_invitation = "ChatDoctor: "
-
-    # Build conversation from history
-    history_text = []
-    for human, assistant in history_context:
-        if human:
-            history_text.append(human_invitation + human)
-        if assistant:
-            history_text.append(doctor_invitation + assistant)
-    
-    # Add current user input
-    history_text.append(human_invitation + user_input)
-
-    # Build conversation prompt
-    prompt = "\n".join(history_text) + "\n" + doctor_invitation
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-
-    # Define stop words and their token IDs
-    stop_words = ["Patient:", "\nPatient:", "Patient :", "\n\nPatient"]
-    stop_ids = [tokenizer.encode(word, add_special_tokens=False) for word in stop_words]
-    stopping_criteria = StoppingCriteriaList([StopOnTokens(stop_ids)])
-
-    # Generate model response
-    with torch.no_grad():
-        output_ids = generator(
-            input_ids,
-            max_new_tokens=MAX_NEW_TOKENS,
-            do_sample=True,
-            temperature=TEMPERATURE,
-            top_k=TOP_K,
-            repetition_penalty=REPETITION_PENALTY,
-            stopping_criteria=stopping_criteria,
-            pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id
-        )
-
-    # Decode and clean response
-    full_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    response = full_output[len(prompt):].strip()
-    
-    # Remove any "Patient:" that might have slipped through
-    for stop_word in ["Patient:", "Patient :", "\nPatient:", "\nPatient", "Patient"]:
-        if stop_word in response:
-            response = response.split(stop_word)[0].strip()
-            break
-
-    response = response.strip()
-
-    # Free memory
-    del input_ids, output_ids
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    return response
-
-# =============================
-# Gradio Chat Function
-# =============================
-def chat_function(message, history):
-    """Gradio chat interface function"""
-    if not message.strip():
-        return ""
-    
-    try:
-        response = get_response(message, history)
-        return response
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# =============================
-# Custom CSS
-# =============================
-custom_css = """
-#header {
-    text-align: center;
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    color: white;
-    padding: 20px;
-    border-radius: 10px;
-    margin-bottom: 20px;
-}
-
-#header h1 {
-    margin: 0;
-    font-size: 2.5em;
-}
-
-#header p {
-    margin: 10px 0 0 0;
-    font-size: 1.1em;
-    opacity: 0.9;
-}
-
-.disclaimer {
-    background-color: #fff3cd;
-    border: 1px solid #ffc107;
-    border-radius: 8px;
-    padding: 15px;
-    margin: 20px 0;
-    color: #856404;
-}
-
-.disclaimer h3 {
-    margin-top: 0;
-    color: #856404;
-}
-
-footer {
-    text-align: center;
-    margin-top: 30px;
-    color: #666;
-    font-size: 0.9em;
-}
-"""
-
-# =============================
-# Gradio Interface
-# =============================
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    # Header
-    gr.HTML("""
-        <div id="header">
-            <h1>🩺 ChatDoctor AI Assistant</h1>
-            <p>Your AI-powered medical conversation partner</p>
-        </div>
-    """)
-    
-    # Disclaimer
-    gr.HTML("""
-        <div class="disclaimer">
-            <h3>⚠️ Medical Disclaimer</h3>
-            <p><strong>Important:</strong> This AI assistant is for informational and educational purposes only. 
-            It is NOT a substitute for professional medical advice, diagnosis, or treatment. 
-            Always seek the advice of your physician or other qualified health provider with any questions 
-            you may have regarding a medical condition. Never disregard professional medical advice or 
-            delay in seeking it because of something you have read here.</p>
-        </div>
-    """)
-    
-    # Chatbot Interface
-    chatbot = gr.Chatbot(
-        height=500,
-        placeholder="<div style='text-align: center; padding: 40px;'><h3>👋 Welcome to ChatDoctor!</h3><p>I'm here to discuss your health concerns. How can I assist you today?</p></div>",
-        show_label=False,
-        avatar_images=(None, "🤖"),
-    )
-    
-    with gr.Row():
-        msg = gr.Textbox(
-            placeholder="Type your message here... (e.g., 'I have a headache')",
-            show_label=False,
-            scale=9,
-            container=False
-        )
-        submit_btn = gr.Button("Send 📤", scale=1, variant="primary")
-    
-    with gr.Row():
-        clear_btn = gr.Button("🗑️ Clear Chat", scale=1)
-        retry_btn = gr.Button("🔄 Retry", scale=1)
-    
-    # Examples
-    gr.Examples(
-        examples=[
-            "I have a persistent headache for 3 days. What should I do?",
-            "What are the symptoms of diabetes?",
-            "How can I improve my sleep quality?",
-            "I have a fever and sore throat. Should I be concerned?",
-            "What are some natural ways to reduce stress?",
-        ],
-        inputs=msg,
-        label="💡 Example Questions"
-    )
-    
-    # Settings (collapsed by default)
-    with gr.Accordion("⚙️ Advanced Settings", open=False):
-        temperature_slider = gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=TEMPERATURE,
-            step=0.1,
-            label="Temperature (Creativity)",
-            info="Higher values make responses more creative but less focused"
-        )
-        max_tokens_slider = gr.Slider(
-            minimum=50,
-            maximum=500,
-            value=MAX_NEW_TOKENS,
-            step=50,
-            label="Max Response Length",
-            info="Maximum number of tokens in response"
-        )
-        top_k_slider = gr.Slider(
-            minimum=1,
-            maximum=100,
-            value=TOP_K,
-            step=1,
-            label="Top K",
-            info="Limits vocabulary selection"
-        )
-    
-    # Footer
-    gr.HTML("""
-        <footer>
-            <p>Powered by ChatDoctor Model | Built with Gradio</p>
-            <p>Device: """ + device.upper() + """ | Model: LLaMA-based Medical AI</p>
-        </footer>
-    """)
-    
-    # Event handlers
-    def user_message(user_msg, history):
-        return "", history + [[user_msg, None]]
-    
-    def bot_response(history, temp, max_tok, top_k_val):
-        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
-        TEMPERATURE = temp
-        MAX_NEW_TOKENS = int(max_tok)
-        TOP_K = int(top_k_val)
-        
-        user_msg = history[-1][0]
-        bot_msg = chat_function(user_msg, history[:-1])
-        history[-1][1] = bot_msg
-        return history
-    
-    # Connect events
-    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, [chatbot, temperature_slider, max_tokens_slider, top_k_slider], chatbot
-    )
-    
-    submit_btn.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, [chatbot, temperature_slider, max_tokens_slider, top_k_slider], chatbot
-    )
-    
-    clear_btn.click(lambda: None, None, chatbot, queue=False)
-    
-    def retry_last():
-        return None
-    
-    retry_btn.click(retry_last, None, chatbot, queue=False)
-
-# =============================
-# Launch Interface
-# =============================
-if __name__ == "__main__":
-    print("\n🚀 Launching ChatDoctor Gradio Interface...")
-    demo.queue()
-    demo.launch(
-        server_name="0.0.0.0",  # Accessible from network
-        server_port=7860,
-        share=False,  # Set to True to create public link
-        show_error=True
+import os
+import gc
+import torch
+import gradio as gr
+from transformers import LlamaTokenizer, LlamaForCausalLM, StoppingCriteria, StoppingCriteriaList
+
+# =============================
+# Configuration
+# =============================
+MODEL_PATH = r"Muhammadidrees/JayConverstionalModel"
+MAX_NEW_TOKENS = 200
+TEMPERATURE = 0.5
+TOP_K = 50
+REPETITION_PENALTY = 1.1
+
+# Detect device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Loading model from {MODEL_PATH} on {device}...")
+
+# =============================
+# Load Tokenizer and Model
+# =============================
+tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
+model = LlamaForCausalLM.from_pretrained(
+    MODEL_PATH,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True
+)
+
+generator = model.generate
+print("✅ ChatDoctor model loaded successfully!\n")
+
+# =============================
+# Stopping Criteria
+# =============================
+class StopOnTokens(StoppingCriteria):
+    def __init__(self, stop_ids):
+        self.stop_ids = stop_ids
+
+    def __call__(self, input_ids, scores, **kwargs):
+        for stop_id_seq in self.stop_ids:
+            if len(stop_id_seq) == 1:
+                if input_ids[0][-1] == stop_id_seq[0]:
+                    return True
+            else:
+                if len(input_ids[0]) >= len(stop_id_seq):
+                    if input_ids[0][-len(stop_id_seq):].tolist() == stop_id_seq:
+                        return True
+        return False
+
+# =============================
+# Chat History (Global)
+# =============================
+conversation_history = []
+
+# =============================
+# Get Response Function
+# =============================
+def get_response(user_input, history_context):
+    """Generate response from ChatDoctor model"""
+    human_invitation = "Patient: "
+    doctor_invitation = "ChatDoctor: "
+
+    # Build conversation from history
+    history_text = []
+    for human, assistant in history_context:
+        if human:
+            history_text.append(human_invitation + human)
+        if assistant:
+            history_text.append(doctor_invitation + assistant)
+    
+    # Add current user input
+    history_text.append(human_invitation + user_input)
+
+    # Build conversation prompt
+    prompt = "\n".join(history_text) + "\n" + doctor_invitation
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+    # Define stop words and their token IDs
+    stop_words = ["Patient:", "\nPatient:", "Patient :", "\n\nPatient"]
+    stop_ids = [tokenizer.encode(word, add_special_tokens=False) for word in stop_words]
+    stopping_criteria = StoppingCriteriaList([StopOnTokens(stop_ids)])
+
+    # Generate model response
+    with torch.no_grad():
+        output_ids = generator(
+            input_ids,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=TEMPERATURE,
+            top_k=TOP_K,
+            repetition_penalty=REPETITION_PENALTY,
+            stopping_criteria=stopping_criteria,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+
+    # Decode and clean response
+    full_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    response = full_output[len(prompt):].strip()
+    
+    # Remove any "Patient:" that might have slipped through
+    for stop_word in ["Patient:", "Patient :", "\nPatient:", "\nPatient", "Patient"]:
+        if stop_word in response:
+            response = response.split(stop_word)[0].strip()
+            break
+
+    response = response.strip()
+
+    # Free memory
+    del input_ids, output_ids
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    return response
+
+# =============================
+# Gradio Chat Function
+# =============================
+def chat_function(message, history):
+    """Gradio chat interface function"""
+    if not message.strip():
+        return ""
+    
+    try:
+        response = get_response(message, history)
+        return response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# =============================
+# Custom CSS
+# =============================
+custom_css = """
+#header {
+    text-align: center;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 20px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+
+#header h1 {
+    margin: 0;
+    font-size: 2.5em;
+}
+
+#header p {
+    margin: 10px 0 0 0;
+    font-size: 1.1em;
+    opacity: 0.9;
+}
+
+.disclaimer {
+    background-color: #fff3cd;
+    border: 1px solid #ffc107;
+    border-radius: 8px;
+    padding: 15px;
+    margin: 20px 0;
+    color: #856404;
+}
+
+.disclaimer h3 {
+    margin-top: 0;
+    color: #856404;
+}
+
+footer {
+    text-align: center;
+    margin-top: 30px;
+    color: #666;
+    font-size: 0.9em;
+}
+"""
+
+# =============================
+# Gradio Interface
+# =============================
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+    # Header
+    gr.HTML("""
+        <div id="header">
+            <h1>🩺 ChatDoctor AI Assistant</h1>
+            <p>Your AI-powered medical conversation partner</p>
+        </div>
+    """)
+    
+    # Disclaimer
+    gr.HTML("""
+        <div class="disclaimer">
+            <h3>⚠️ Medical Disclaimer</h3>
+            <p><strong>Important:</strong> This AI assistant is for informational and educational purposes only. 
+            It is NOT a substitute for professional medical advice, diagnosis, or treatment. 
+            Always seek the advice of your physician or other qualified health provider with any questions 
+            you may have regarding a medical condition. Never disregard professional medical advice or 
+            delay in seeking it because of something you have read here.</p>
+        </div>
+    """)
+    
+    # Chatbot Interface
+    chatbot = gr.Chatbot(
+        height=500,
+        placeholder="<div style='text-align: center; padding: 40px;'><h3>👋 Welcome to ChatDoctor!</h3><p>I'm here to discuss your health concerns. How can I assist you today?</p></div>",
+        show_label=False,
+        avatar_images=(None, "🤖"),
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            placeholder="Type your message here... (e.g., 'I have a headache')",
+            show_label=False,
+            scale=9,
+            container=False
+        )
+        submit_btn = gr.Button("Send 📤", scale=1, variant="primary")
+    
+    with gr.Row():
+        clear_btn = gr.Button("🗑️ Clear Chat", scale=1)
+        retry_btn = gr.Button("🔄 Retry", scale=1)
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            "I have a persistent headache for 3 days. What should I do?",
+            "What are the symptoms of diabetes?",
+            "How can I improve my sleep quality?",
+            "I have a fever and sore throat. Should I be concerned?",
+            "What are some natural ways to reduce stress?",
+        ],
+        inputs=msg,
+        label="💡 Example Questions"
+    )
+    
+    # Settings (collapsed by default)
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        temperature_slider = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=TEMPERATURE,
+            step=0.1,
+            label="Temperature (Creativity)",
+            info="Higher values make responses more creative but less focused"
+        )
+        max_tokens_slider = gr.Slider(
+            minimum=50,
+            maximum=500,
+            value=MAX_NEW_TOKENS,
+            step=50,
+            label="Max Response Length",
+            info="Maximum number of tokens in response"
+        )
+        top_k_slider = gr.Slider(
+            minimum=1,
+            maximum=100,
+            value=TOP_K,
+            step=1,
+            label="Top K",
+            info="Limits vocabulary selection"
+        )
+    
+    # Footer
+    gr.HTML("""
+        <footer>
+            <p>Powered by ChatDoctor Model | Built with Gradio</p>
+            <p>Device: """ + device.upper() + """ | Model: LLaMA-based Medical AI</p>
+        </footer>
+    """)
+    
+    # Event handlers
+    def user_message(user_msg, history):
+        return "", history + [[user_msg, None]]
+    
+    def bot_response(history, temp, max_tok, top_k_val):
+        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
+        TEMPERATURE = temp
+        MAX_NEW_TOKENS = int(max_tok)
+        TOP_K = int(top_k_val)
+        
+        user_msg = history[-1][0]
+        bot_msg = chat_function(user_msg, history[:-1])
+        history[-1][1] = bot_msg
+        return history
+    
+    # Connect events
+    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, [chatbot, temperature_slider, max_tokens_slider, top_k_slider], chatbot
+    )
+    
+    submit_btn.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, [chatbot, temperature_slider, max_tokens_slider, top_k_slider], chatbot
+    )
+    
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
+    
+    def retry_last():
+        return None
+    
+    retry_btn.click(retry_last, None, chatbot, queue=False)
+
+# =============================
+# Launch Interface
+# =============================
+if __name__ == "__main__":
+    print("\n🚀 Launching ChatDoctor Gradio Interface...")
+    demo.queue()
+    demo.launch(
+        server_name="0.0.0.0",  # Accessible from network
+        server_port=7860,
+        share=False,  # Set to True to create public link
+        show_error=True
     )