Update app.py

app.py

@@ -1,17 +1,16 @@
+# app.py
 import gradio as gr
-import torch
 from transformers import AutoProcessor, AutoModelForVision2Seq
-from PaitentVoiceToText import record_and_transcribe
-from DocVoice import text_to_speech  # Your TTS function
+import torch
 
 # -------------------
-# 1️⃣ Load Model & Processor
+# 1️⃣ Load Model
 # -------------------
 def load_model():
     device = "cuda" if torch.cuda.is_available() else "cpu"
     dtype = torch.float16 if device == "cuda" else torch.float32
 
-    # Load directly from Hugging Face
+    # Load model and processor from Hugging Face
     processor = AutoProcessor.from_pretrained("Muhammadidrees/RaiyaChatDoc", trust_remote_code=True)
     model = AutoModelForVision2Seq.from_pretrained(
         "Muhammadidrees/RaiyaChatDoc",
@@ -24,7 +23,7 @@ def load_model():
 processor, model, device = load_model()
 
 # -------------------
-# 2️⃣ Chat Logic Functions
+# 2️⃣ Chat Logic
 # -------------------
 def process_message(message, history, question_count):
     if not message.strip():
@@ -32,40 +31,32 @@ def process_message(message, history, question_count):
     
     history.append([message, None])
     question_count += 1
-    
-    should_analyze = (
-        question_count >= 6 or 
-        any(word in message.lower() for word in ["analysis", "diagnose", "what do you think", "causes"])
+
+    # Decide if analysis is needed
+    should_analyze = question_count >= 6 or any(
+        word in message.lower() for word in ["analysis", "diagnose", "what do you think", "causes"]
     )
 
-    if should_analyze:
-        system_prompt = (
-            "You are a medical doctor. Based on the patient's responses, provide a comprehensive analysis "
-            "of potential causes for their symptoms. Start with 'Based on the information provided by the patient, "
-            "potential causes of [symptoms] could include:' and list 3-4 possible diagnoses with brief explanations. "
-            "Format as numbered list with diagnosis name and short explanation."
-        )
-    else:
-        system_prompt = (
-            "You are a medical doctor conducting a patient interview. Ask ONE specific, direct medical question "
-            "to gather important diagnostic information. Keep it brief - just ask the question without explanations. "
-            "Focus on key areas like: age, medical history, medications, lifestyle, family history, or symptom details."
-        )
-    
+    # System prompt
+    system_prompt = (
+        "You are a medical doctor. "
+        "Provide a comprehensive analysis of potential causes for symptoms."
+        if should_analyze else
+        "You are a medical doctor conducting a patient interview. Ask ONE specific question."
+    )
+
+    # Build conversation context
     dialogue = []
     for user_msg, bot_msg in history[:-1]:
-        if user_msg:
-            dialogue.append(f"Patient: {user_msg}")
-        if bot_msg:
-            dialogue.append(f"Doctor: {bot_msg}")
+        if user_msg: dialogue.append(f"Patient: {user_msg}")
+        if bot_msg: dialogue.append(f"Doctor: {bot_msg}")
     dialogue.append(f"Patient: {message}")
-    
-    conversation = "\n".join(dialogue)
-    prompt = f"{system_prompt}\n\nConversation:\n{conversation}\nDoctor:"
+    prompt = f"{system_prompt}\n\nConversation:\n" + "\n".join(dialogue) + "\nDoctor:"
 
+    # Prepare input
     inputs = processor(text=prompt, images=None, return_tensors="pt").to(device)
-    max_tokens = 1000 if should_analyze else 25
-    
+    max_tokens = 400 if should_analyze else 25
+
     with torch.inference_mode():
         outputs = model.generate(
             **inputs,
@@ -74,39 +65,22 @@ def process_message(message, history, question_count):
             temperature=0.6,
             top_p=0.9,
             repetition_penalty=1.1,
-            pad_token_id=processor.tokenizer.eos_token_id,
+            pad_token_id=processor.tokenizer.eos_token_id
         )
 
+    # Decode response
     input_length = inputs["input_ids"].shape[1]
-    generated_tokens = outputs[:, input_length:]
-    response = processor.batch_decode(generated_tokens, skip_special_tokens=True)[0].strip()
-
+    response = processor.batch_decode(outputs[:, input_length:], skip_special_tokens=True)[0].strip()
     if response.lower().startswith("doctor:"):
         response = response[7:].strip()
 
+    # Concise question formatting
     if not should_analyze:
-        sentences = response.split('?')
-        if len(sentences) > 1:
-            response = sentences[0].strip() + '?'
-        cleanup_starts = [
-            "I need to ask",
-            "Let me ask",
-            "I would like to know",
-            "Can you tell me",
-            "It would help if",
-        ]
-        for phrase in cleanup_starts:
-            if response.startswith(phrase):
-                parts = response.split(',', 1)
-                if len(parts) > 1:
-                    response = parts[1].strip()
-                    if not response.endswith('?'):
-                        response += '?'
+        response = response.split('?')[0].strip() + '?'
 
     history[-1][1] = response
-    if should_analyze:
-        question_count = 0
-    
+    if should_analyze: question_count = 0
+
     return history, history, question_count
 
 def force_analysis(history, question_count):
@@ -116,124 +90,35 @@ def clear_chat():
     return [], [], 0
 
 # -------------------
-# 3️⃣ TTS Helper
+# 3️⃣ Gradio Interface
 # -------------------
-def play_assistant_audio(response_text):
-    if response_text:
-        text_to_speech(response_text)
-    return None
-
-# -------------------
-# 4️⃣ Gradio Interface
-# -------------------
-with gr.Blocks(title="ChatDOC", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="ChatDOC") as demo:
     question_count_state = gr.State(0)
-    assistant_responses_state = gr.State([])
-
-    gr.Markdown(
-        """
-        # 🩺 Chat with ChatDOC
-        Welcome! I'm your AI medical assistant. Please describe your symptoms and I'll ask relevant questions to help understand your condition better.
-        """
-    )
     
-    chatbot = gr.Chatbot(
-        value=[],
-        height=400,
-        show_label=False,
-        avatar_images=(
-            r"C:\Users\JAY\Downloads\model\user_msg.png",
-            r"C:\Users\JAY\Downloads\model\bot_msg.jpg"
-        ),
-        bubble_full_width=False
-    )
+    gr.Markdown("# 🩺 Chat with ChatDOC\nDescribe your symptoms and get guidance.")
+    chatbot = gr.Chatbot(value=[], height=400, show_label=False)
     
     with gr.Row():
-        msg = gr.Textbox(
-            placeholder="Describe your symptoms...",
-            scale=4,
-            container=False,
-            show_label=False
-        )
+        msg = gr.Textbox(placeholder="Describe your symptoms...", scale=4, container=False, show_label=False)
         send_btn = gr.Button("Send", variant="primary", scale=1)
-        mic_btn = gr.Button("🎤 Speak", variant="secondary", scale=1)
     
     with gr.Row():
         analysis_btn = gr.Button("Request Analysis", variant="secondary")
         clear_btn = gr.Button("Clear Chat", variant="stop")
-        play_audio_btn = gr.Button("🔊 Play Assistant Response", variant="secondary")
     
-    # -------------------
-    # Update assistant responses
-    # -------------------
-    def update_assistant_responses(history, assistant_responses):
-        if history and history[-1][1]:
-            assistant_responses.append(history[-1][1])
-        return assistant_responses
-
-    # -------------------
-    # Submit handlers
-    # -------------------
-    def user_submit(message, history, question_count, assistant_responses):
-        history, updated_history, question_count = process_message(message, history, question_count)
-        assistant_responses = update_assistant_responses(history, assistant_responses)
-        return updated_history, updated_history, question_count, assistant_responses
-
-    def mic_submit(history, question_count, assistant_responses):
-        user_text = record_and_transcribe(duration=5)
-        history.append([user_text, None])
-        history, updated_history, question_count = process_message(user_text, history, question_count)
-        assistant_responses = update_assistant_responses(history, assistant_responses)
-        return updated_history, updated_history, question_count, assistant_responses
-
-    def clear_input():
-        return ""
-    
-    # -------------------
-    # Connect buttons
-    # -------------------
-    send_btn.click(
-        user_submit,
-        inputs=[msg, chatbot, question_count_state, assistant_responses_state],
-        outputs=[chatbot, chatbot, question_count_state, assistant_responses_state]
-    ).then(clear_input, outputs=[msg])
+    send_event = send_btn.click(
+        process_message, inputs=[msg, chatbot, question_count_state], outputs=[chatbot, chatbot, question_count_state]
+    ).then(lambda: "", outputs=[msg])
     
     msg.submit(
-        user_submit,
-        inputs=[msg, chatbot, question_count_state, assistant_responses_state],
-        outputs=[chatbot, chatbot, question_count_state, assistant_responses_state]
-    ).then(clear_input, outputs=[msg])
+        process_message, inputs=[msg, chatbot, question_count_state], outputs=[chatbot, chatbot, question_count_state]
+    ).then(lambda: "", outputs=[msg])
     
-    mic_btn.click(
-        mic_submit,
-        inputs=[chatbot, question_count_state, assistant_responses_state],
-        outputs=[chatbot, chatbot, question_count_state, assistant_responses_state]
-    )
-    
-    analysis_btn.click(
-        force_analysis,
-        inputs=[chatbot, question_count_state],
-        outputs=[chatbot, question_count_state]
-    )
-    
-    clear_btn.click(
-        clear_chat,
-        outputs=[chatbot, chatbot, question_count_state]
-    )
-
-    play_audio_btn.click(
-        lambda assistant_responses: play_assistant_audio(assistant_responses[-1]) if assistant_responses else None,
-        inputs=[assistant_responses_state],
-        outputs=[]
-    )
+    analysis_btn.click(force_analysis, inputs=[chatbot, question_count_state], outputs=[chatbot, question_count_state])
+    clear_btn.click(clear_chat, outputs=[chatbot, chatbot, question_count_state])
 
 # -------------------
-# 5️⃣ Launch
+# 4️⃣ Launch
 # -------------------
 if __name__ == "__main__":
-    demo.launch(
-        server_name="127.0.0.1",
-        server_port=7860,
-        share=False,
-        debug=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)