Update app.py

app.py

@@ -3,33 +3,33 @@ import requests
 from PIL import Image
 from io import BytesIO
 import os
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
 
-# ✅ Load API key from environment
+# ✅ Load Groq API key from environment (add this in Hugging Face Secrets)
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 GROQ_MODEL = "llama3-70b-8192"
 
-# 🔧 Simulated caption (replace later)
+# ✅ Load BLIP model for image captioning
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# 📸 Generate caption from image
+
 def get_caption(image):
-    return "This image shows a large crack in the wall extending diagonally."
+    inputs = processor(images=image, return_tensors="pt")
+    out = blip_model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
+
+# 💬 Communicate with Groq API
 
-# 💬 Send prompt to Groq
 def generate_response(image, user_query=""):
     if GROQ_API_KEY is None:
-        return "❌ API key not set. Please set GROQ_API_KEY using os.environ."
+        return "❌ API key not set. Please set GROQ_API_KEY using Hugging Face Secrets."
 
     caption = get_caption(image)
 
-    prompt = f"""
-You are a construction damage analysis assistant. Based on the description: "{caption}", and user question: "{user_query}", provide the following:
-1. Type of damage
-2. Likely cause
-3. Suggested repair solutions
-4. Required tools
-5. Estimated time and cost
-
-Respond in a helpful tone.
-"""
-
     headers = {
         "Authorization": f"Bearer {GROQ_API_KEY}",
         "Content-Type": "application/json"
@@ -37,15 +37,23 @@ Respond in a helpful tone.
 
     data = {
         "model": GROQ_MODEL,
-        "messages": [{"role": "user", "content": prompt}]
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant that analyzes construction damage from image captions and gives expert repair advice."},
+            {"role": "user", "content": f"Image caption: {caption}"},
+            {"role": "user", "content": f"My question is: {user_query or 'No follow-up question.'}"},
+            {"role": "user", "content": "Please provide:\n1. Type of damage\n2. Likely cause\n3. Suggested repair solutions\n4. Required tools\n5. Estimated time and cost"}
+        ]
     }
 
-    response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data)
-    
-    if response.status_code == 200:
-        return response.json()['choices'][0]['message']['content']
-    else:
-        return f"❌ Error {response.status_code}: {response.text}"
+    try:
+        response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data, timeout=30)
+        if response.status_code == 200:
+            content = response.json()['choices'][0]['message']['content']
+            return content if content.strip() else "⚠️ No response received. Please try again."
+        else:
+            return f"❌ Error {response.status_code}: {response.text}"
+    except Exception as e:
+        return f"❌ Exception: {str(e)}"
 
 # ============================
 # 🌟 Gradio Modern UI
@@ -78,17 +86,17 @@ with gr.Blocks(css="""
 }
 """) as demo:
 
-    gr.Markdown("<div id='title'>🏗️ BuildFix AI — Construction Damage Inspector</div>")
+    gr.Markdown("<div id='title'>\ud83c\udfd7\ufe0f BuildFix AI — Construction Damage Inspector</div>")
     gr.Markdown("<div id='subtitle'>Upload a construction damage image and get expert analysis with repair suggestions instantly.</div>")
 
     with gr.Row():
         with gr.Column(scale=1, elem_classes="card"):
-            image_input = gr.Image(type="pil", label="📸 Upload Image of Damage")
-            user_query = gr.Textbox(label="💬 Ask a follow-up question (optional)", placeholder="e.g., How expensive is this repair?", lines=2)
-            submit_btn = gr.Button("🔍 Analyze", variant="primary")
+            image_input = gr.Image(type="pil", label="\ud83d\udcf8 Upload Image of Damage")
+            user_query = gr.Textbox(label="\ud83d\udcac Ask a follow-up question (optional)", placeholder="e.g., How expensive is this repair?", lines=2)
+            submit_btn = gr.Button("\ud83d\udd0d Analyze", variant="primary")
 
         with gr.Column(scale=1, elem_classes="card"):
-            result_output = gr.Textbox(label="🧠 AI Report", lines=20)
+            result_output = gr.Textbox(label="\ud83e\uddd0 AI Report", lines=20)
 
     submit_btn.click(fn=generate_response, inputs=[image_input, user_query], outputs=result_output)