Update app.py

app.py

@@ -5,10 +5,11 @@ from pathlib import Path
 import tempfile
 import time
 from groq import Groq
+import requests  # We need to keep requests for Kimi K2
 
 # --- Configuration ---
-# NOTE: In a real production app, use environment variables for keys!
 GROQ_CLIENT = Groq(api_key=os.environ.get('groq'))
+HYPERBOLIC_KEY = os.environ.get('h_api_key')  # For Kimi K2
 
 class MediClearBackend:
     """Handles the API logic to keep the UI code clean."""
@@ -20,11 +21,10 @@ class MediClearBackend:
 
     @staticmethod
     def analyze_medical_image(image_path):
-        """Step 1: Extract technical info using Groq VLM"""
+        """Step 1: Extract technical info using Groq VLM (Llama 4 Scout)"""
         try:
             base64_image = MediClearBackend.encode_image(image_path)
             
-            # Create the message content with image
             messages = [
                 {
                     "role": "user",
@@ -35,18 +35,16 @@ class MediClearBackend:
                 }
             ]
 
-            # Use Groq API with streaming
             completion = GROQ_CLIENT.chat.completions.create(
                 model="meta-llama/llama-4-scout-17b-16e-instruct",
                 messages=messages,
-                temperature=0.1,  # Lower temperature for more consistent medical analysis
+                temperature=0.1,
                 max_completion_tokens=1024,
                 top_p=0.9,
                 stream=True,
                 stop=None
             )
 
-            # Collect the streamed response
             technical_data = ""
             for chunk in completion:
                 if chunk.choices[0].delta.content:
@@ -58,51 +56,44 @@ class MediClearBackend:
 
     @staticmethod
     def summarize_for_patient(technical_text):
-        """Step 2: Convert technical info to patient-friendly text using Groq"""
+        """Step 2: Convert technical info to patient-friendly text using Kimi K2 via Hyperbolic"""
         try:
+            if not HYPERBOLIC_KEY:
+                raise Exception("Hyperbolic API key not configured")
+                
+            url = "https://api.hyperbolic.xyz/v1/chat/completions"
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {HYPERBOLIC_KEY}"
+            }
+            
             system_prompt = """You are a senior medical doctor with 50 years of experience. 
             Your role is to explain medical reports to patients in simple, reassuring, everyday language.
             Never mention the doctors name whatsoever.
-            
-            Structure your response as follows:
-            1. Start with a warm, reassuring greeting
-            2. Provide an overall assessment in simple terms
-            3. Explain key findings and what they mean compared to normal
-            4. Suggest next steps or recommendations
-            5. End with a reassuring closing statement
-            
-            Speak directly to the patient in a compassionate tone. Avoid medical jargon unless necessary, and when you use it, explain it clearly."""
+            Structure:
+            1. Warm greeting.
+            2. Overall assessment.
+            3. Key findings (compared to normal).
+            4. Next steps.
+            5. Reassuring closing.
+            Speak directly to the patient."""
 
-            messages = [
-                {
-                    "role": "system", 
-                    "content": system_prompt
-                },
-                {
-                    "role": "user", 
-                    "content": f"Please explain this medical analysis in simple, patient-friendly terms:\n\n{technical_text}"
-                }
-            ]
+            data = {
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": technical_text}
+                ],
+                "model": "moonshotai/Kimi-K2-Instruct",  # This is the Kimi K2 model
+                "max_tokens": 4096,
+                "temperature": 0.1,
+                "top_p": 0.9
+            }
 
-            completion = GROQ_CLIENT.chat.completions.create(
-                model="meta-llama/llama-4-scout-17b-16e-instruct",
-                messages=messages,
-                temperature=0.3,  # Slightly higher temperature for more natural patient communication
-                max_completion_tokens=1024,
-                top_p=0.9,
-                stream=True,
-                stop=None
-            )
-
-            # Collect the streamed response
-            patient_summary = ""
-            for chunk in completion:
-                if chunk.choices[0].delta.content:
-                    patient_summary += chunk.choices[0].delta.content
-
-            return patient_summary
+            response = requests.post(url, headers=headers, json=data)
+            response.raise_for_status()
+            return response.json()['choices'][0]['message']['content']
         except Exception as e:
-            raise Exception(f"Summary Error: {str(e)}")
+            raise Exception(f"Summary Error (Kimi K2): {str(e)}")
 
 def process_single_image(image_file):
     """Process a single medical image and return the analysis"""
@@ -112,15 +103,10 @@ def process_single_image(image_file):
         return "❌ Please upload a medical image first."
     
     try:
-        # Create progress updates
-        progress_updates = []
-        
-        # Step 1: Technical analysis
-        progress_updates.append("🩺 Step 1/2: Scanning image for medical details...")
+        # Step 1: Technical analysis with Groq (Llama 4 Scout)
         technical_data = backend.analyze_medical_image(image_file.name)
         
-        # Step 2: Patient-friendly summary
-        progress_updates.append("📝 Step 2/2: Generating patient-friendly summary...")
+        # Step 2: Patient-friendly summary with Kimi K2
         final_report = backend.summarize_for_patient(technical_data)
         
         # Format the final report
@@ -129,6 +115,8 @@ def process_single_image(image_file):
 {'='*60}
 🩺 MEDICAL ANALYSIS REPORT: {filename}
 {'='*60}
+🔬 Technical Analysis (Llama 4 Scout): Medical image processed
+📝 Patient Summary (Kimi K2): 
 {final_report}
 {'='*60}
 ✅ Analysis Complete - This tool provides AI-powered insights and is not a substitute for professional medical diagnosis.
@@ -146,14 +134,16 @@ def process_multiple_images(image_files):
     if not image_files:
         return "❌ Please upload at least one medical image."
     
-    full_report = f"🩺 SEHATSCAN - COMPREHENSIVE MEDICAL ANALYSIS\n{'='*70}\n\n"
+    full_report = f"🩺 SEHATSCAN - COMPREHENSIVE MEDICAL ANALYSIS\n"
+    full_report += f"🔬 Step 1: Groq (Llama 4 Scout) | 📝 Step 2: Hyperbolic (Kimi K2)\n"
+    full_report += f"{'='*70}\n\n"
+    
     total = len(image_files)
     
     for index, image_file in enumerate(image_files, 1):
         try:
             filename = Path(image_file.name).name
             full_report += f"\n📄 IMAGE {index}/{total}: {filename}\n{'-'*50}\n"
-            full_report += "🔍 Analyzing medical image...\n"
             
             # Process the image
             technical_data = backend.analyze_medical_image(image_file.name)
@@ -233,7 +223,7 @@ def create_gradio_interface():
                 """
                 # 🩺 SehatScan - Koshur AI
                 ### Professional Medical Image Analysis
-                **Powered by Groq & Llama 4 Scout**
+                **🔬 Step 1: Groq (Llama 4 Scout) | 📝 Step 2: Hyperbolic (Kimi K2)**
                 """
             )
         
@@ -276,13 +266,13 @@ def create_gradio_interface():
                     # Clear button
                     clear_btn = gr.Button("🗑️ Clear All", variant="stop")
                 
-                # Processing Info
+                # Model Info
                 with gr.Group(elem_classes="processing"):
                     gr.Markdown(
                         """
-                        ⚡ **Fast Processing**
-                        Using Groq's accelerated inference for quick medical image analysis.
-                        Typical processing time: 10-30 seconds per image.
+                        🤖 **AI Models Used:**
+                        - **Step 1**: Groq + Llama 4 Scout (Medical Image Analysis)
+                        - **Step 2**: Hyperbolic + Kimi K2 (Patient Communication)
                         """
                     )
                 
@@ -324,7 +314,7 @@ def create_gradio_interface():
                     """
                 )
         
-        # Event handlers for single image analysis
+        # Event handlers
         analyze_single_btn.click(
             fn=process_single_image,
             inputs=[single_image],
@@ -332,7 +322,6 @@ def create_gradio_interface():
             api_name="analyze_single"
         )
         
-        # Event handlers for multiple image analysis
         analyze_batch_btn.click(
             fn=process_multiple_images,
             inputs=[multiple_images],
@@ -359,7 +348,7 @@ def create_gradio_interface():
             outputs=[output_text]
         )
         
-        # Export functionality (simulated - in Gradio, users can copy from textbox)
+        # Export functionality
         def export_report(text):
             if text:
                 timestamp = time.strftime("%Y%m%d-%H%M%S")
@@ -373,12 +362,12 @@ def create_gradio_interface():
             outputs=[output_text]
         )
         
-        # Examples section (commented out as we don't have example images)
+        # Instructions
         gr.Markdown("### 🎯 How to use:")
         gr.Markdown("""
         1. **Upload** a medical image (X-ray, MRI, CT scan, ultrasound, etc.)
         2. **Click Analyze** to get AI-powered medical insights
-        3. **Review** the patient-friendly explanation
+        3. **Review** the patient-friendly explanation (powered by Kimi K2)
         4. **Always consult** with healthcare professionals for medical decisions
         """)