Create app.py

app.py

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+import gradio as gr
+import base64
+import requests
+import os
+
+# === STEP 1: Get Groq API Key from HF Space Secret ===
+GROQ_API_KEY = os.getenv("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")  # Define this in your Space settings!
+GROQ_MODEL = "llama3-70b-8192"
+GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+
+
+# === STEP 2: Convert image to base64 ===
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        image_bytes = image_file.read()
+    return base64.b64encode(image_bytes).decode("utf-8")
+
+
+# === STEP 3: Build the LLM Prompt ===
+def build_prompt(image_b64: str) -> str:
+    return f"""
+You are an expert in Non-Destructive Testing (NDT) and industrial defect analysis.
+
+A user has uploaded an image of a defected component. Here's the base64 representation of that image (partial for size): {image_b64[:300]}...
+
+Analyze the defect and provide structured output with the following format:
+
+1. Defect Type:
+2. Recommended NDT Technique(s):
+3. Explanation of Defect Cause:
+4. Proposed Solution/Fix:
+5. Estimated Time to Repair:
+6. Tools Required:
+7. Preventive Measures:
+"""
+
+
+# === STEP 4: Call Groq API ===
+def query_groq(prompt: str) -> str:
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": GROQ_MODEL,
+        "messages": [
+            {"role": "system", "content": "You are a professional NDT inspector."},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.5,
+        "max_tokens": 800
+    }
+
+    response = requests.post(GROQ_API_URL, headers=headers, json=payload)
+
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        return f"Error {response.status_code}: {response.text}"
+
+
+# === STEP 5: Main Function for Gradio ===
+def analyze_defect(image_path):
+    if image_path is None:
+        return "Please upload an image."
+
+    image_b64 = image_to_base64(image_path)
+    prompt = build_prompt(image_b64)
+    response = query_groq(prompt)
+    return response
+
+
+# === STEP 6: Gradio UI ===
+demo = gr.Interface(
+    fn=analyze_defect,
+    inputs=gr.Image(type="filepath", label="Upload Defective Component Image"),
+    outputs=gr.Textbox(label="Defect Analysis Report"),
+    title="🛠️ NDT Defect Analyzer",
+    description="Upload an image of a damaged industrial component. The AI will identify the defect, recommend NDT methods, suggest fixes, tools, and future prevention."
+)
+
+# === STEP 7: Launch for Hugging Face Spaces ===
+if __name__ == "__main__":
+    demo.launch()