Upload main.py

src/main.py

@@ -0,0 +1,41 @@
+import base64
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from groq import Groq
+
+app = FastAPI()
+client = Groq(api_key="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")
+
+class ImageRequest(BaseModel):
+    base64_image: str 
+
+
+@app.post("/explain-chart")
+async def explain_chart(request: ImageRequest):
+    try:
+        completion = client.chat.completions.create(
+            
+            model="meta-llama/llama-4-scout-17b-16e-instruct", 
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "Explain this IPL data chart. What are the key insights?"},
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/png;base64,{request.base64_image}"}
+                        }
+                    ]
+                }
+            ]
+        )
+        return {"explanation": completion.choices[0].message.content}
+    except Exception as e:
+       
+        print(f"Error detail: {e}") 
+        raise HTTPException(status_code=500, detail=str(e))
+    
+# after clicking the button in the Streamlit app, it will send a POST request to this endpoint 
+# with the base64 image, and the FastAPI server will process it and return the AI-generated explanation.
+# this is happening in the background, so the user experience is seamless. 
+