Create main.py

main.py

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+# Define request body model
+class InputText(BaseModel):
+    text: str
+
+# Load pre-trained sentiment analysis pipeline
+pipe = pipeline("text-classification", model="avichr/heBERT_sentiment_analysis")
+
+# Define sentiment analysis endpoint
+@app.post("/analyze_sentiment/")
+async def analyze_sentiment(input_text: InputText):
+    try:
+        # Perform sentiment analysis
+        result = pipe(input_text.text)
+        
+        # Format response
+        response_data = {
+            "text": input_text.text,
+            "sentiment": result[0]["label"],
+            "confidence": result[0]["score"]
+        }
+        
+        # Return response
+        return JSONResponse(content=response_data)
+    except Exception as e:
+        # Return error response if an exception occurs
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+# Homepage endpoint
+@app.get("/")
+async def home():
+    return RedirectResponse(url="/docs")
+
+# Run the FastAPI app
+if __name__ == "__main__":
+    import nest_asyncio
+    nest_asyncio.apply()
+    ngrok_tunnel = ngrok.connect(8000)
+    print("Public URL:", ngrok_tunnel.public_url)
+    try:
+        import uvicorn
+        uvicorn.run(app, host="0.0.0.0", port=8000)
+    except Exception as e:
+        print(e)