Upload app.py with huggingface_hub

app.py

@@ -94,22 +94,27 @@ def voice_chat(audio, history):
         return history
 
 def generate_image(prompt):
-    if not prompt.strip(): return None, 'Please enter a description.'
-    if not HF_TOKEN: return None, 'Error: Add HF_TOKEN to Space Settings Secrets.'
+    if not prompt.strip(): return None, 'Please enter a description.', ''
+    if not HF_TOKEN: return None, 'Error: Add HF_TOKEN to Space Settings Secrets.', ''
     try:
-        # Step 1 - Use Groq to enhance prompt for accuracy
         enhanced = prompt
+        description = ''
         if GROQ_KEY:
             try:
                 client = Groq(api_key=GROQ_KEY)
                 msgs = [
-                    {'role':'system','content':'You are an expert at writing image generation prompts for biomedical and engineering topics. Convert the user request into a highly detailed, specific image generation prompt. Be very specific about colors, materials, labels, dimensions, and visual style. Always say: photorealistic, highly detailed, scientific illustration, white background, labeled diagram, professional medical illustration.'},
-                    {'role':'user','content':'Create an image generation prompt for: ' + prompt + '. Context: SJSU CardioLab uses 27mm SJM Regent bileaflet mechanical heart valves, Sylgard 184 transparent silicone, green laser PIV system, Arduino Uno with stepper motor for TGT, Whatman paper microfluidic uPAD devices for CKD creatinine detection using Jaffe reaction orange-red color.'}
+                    {'role':'system','content':'You are a biomedical visualization expert for SJSU CardioLab. Do two things: 1) Write a clear 2-3 sentence description of what the image will show. 2) Write a detailed image generation prompt. Format your response as: DESCRIPTION: [your description here] PROMPT: [your detailed prompt here]'},
+                    {'role':'user','content':'Create image for: ' + prompt + '. CardioLab context: 27mm SJM Regent bileaflet mechanical heart valve, Sylgard 184 transparent silicone MCL, green laser PIV system, Arduino Uno stepper motor TGT, Whatman paper uPAD microfluidic device, Jaffe reaction orange-red color for CKD creatinine detection.'}
                 ]
-                resp = client.chat.completions.create(model='llama-3.3-70b-versatile', messages=msgs, max_tokens=200)
-                enhanced = resp.choices[0].message.content
-            except: pass
-        # Step 2 - Send enhanced prompt to FLUX
+                resp = client.chat.completions.create(model='llama-3.3-70b-versatile', messages=msgs, max_tokens=300)
+                full_resp = resp.choices[0].message.content
+                if 'DESCRIPTION:' in full_resp and 'PROMPT:' in full_resp:
+                    description = full_resp.split('DESCRIPTION:')[1].split('PROMPT:')[0].strip()
+                    enhanced = full_resp.split('PROMPT:')[1].strip()
+                else:
+                    description = full_resp[:200]
+                    enhanced = 'Highly detailed scientific biomedical illustration: ' + prompt + ', professional medical diagram, photorealistic, high quality, labeled'
+            except: enhanced = prompt
         headers = {'Authorization': 'Bearer ' + HF_TOKEN, 'Content-Type': 'application/json'}
         payload = {'inputs': enhanced, 'parameters': {'num_inference_steps': 8, 'guidance_scale': 7.5}}
         models = [
@@ -121,11 +126,11 @@ def generate_image(prompt):
                 r = requests.post(model_url, headers=headers, json=payload, timeout=60)
                 if r.status_code == 200:
                     img = Image.open(io.BytesIO(r.content))
-                    return img, 'Generated! Enhanced prompt: ' + enhanced[:150] + '...'
+                    return img, 'Image generated successfully!', description
             except: continue
-        return None, 'Models busy. Please try again in 30 seconds.'
+        return None, 'Models busy. Try again in 30 seconds.', description
     except Exception as e:
-        return None, 'Error: ' + str(e)
+        return None, 'Error: ' + str(e), ''
 
 def piv_tool(velocity, shear, hr):
     v = 'HIGH - stenosis risk' if float(velocity)>2.0 else 'NORMAL'
@@ -185,8 +190,9 @@ with gr.Blocks(title='CardioLab AI', css=CSS) as demo:
                 with gr.Column(scale=1):
                     img_btn = gr.Button('Generate Image', variant='primary')
                     img_status = gr.Textbox(label='Status', lines=2)
+            img_desc = gr.Textbox(label='AI Description - What will be generated', lines=3, interactive=False)
             img_output = gr.Image(label='Generated Image', type='pil', height=500)
-            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status])
+            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
             gr.Markdown('**Try:** `27mm bileaflet mechanical heart valve` | `microfluidic paper device for CKD testing` | `blood flow visualization PIV` | `Arduino circuit with stepper motor`')
         with gr.Tab('PIV'):
             with gr.Row():