Add files

app.py

@@ -0,0 +1,125 @@
+import tempfile
+from transformers import pipeline, RobertaForSequenceClassification, RobertaTokenizer
+import gradio as gr
+from fastapi import FastAPI, UploadFile, File, Request, HTTPException
+import os
+import json
+from typing import Optional, Dict, List
+import torch
+
+# Initialize models
+model_name = "cardiffnlp/twitter-roberta-base-emotion"
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(model_name)
+emotion_analysis = pipeline("text-classification",
+                          model=model, 
+                          tokenizer=tokenizer,
+                          return_all_scores=True)
+
+# Create FastAPI app
+app = FastAPI()
+
+def save_upload_file(upload_file: UploadFile) -> str:
+    """Save uploaded file to temporary location"""
+    try:
+        suffix = os.path.splitext(upload_file.filename)[1]
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            content = upload_file.file.read()
+            if suffix == '.json':
+                content = content.decode('utf-8')  # Decode JSON files
+            tmp.write(content if isinstance(content, bytes) else content.encode())
+            return tmp.name
+    finally:
+        upload_file.file.close()
+
+@app.post("/api/predict")
+async def predict_from_upload(file: UploadFile = File(...)):
+    """API endpoint for file uploads"""
+    try:
+        # Save the uploaded file temporarily
+        temp_path = save_upload_file(file)
+        
+        # Process based on file type
+        if temp_path.endswith('.json'):
+            with open(temp_path, 'r') as f:
+                data = json.load(f)
+            text = data.get('description', '')
+        else:  # Assume text file
+            with open(temp_path, 'r') as f:
+                text = f.read()
+        
+        if not text.strip():
+            raise HTTPException(status_code=400, detail="No text content found")
+        
+        # Analyze text
+        result = emotion_analysis(text)
+        emotions = [{'label': e['label'], 'score': float(e['score'])} 
+                   for e in sorted(result[0], key=lambda x: x['score'], reverse=True)]
+        
+        # Clean up
+        os.unlink(temp_path)
+        
+        return {
+            "success": True,
+            "results": emotions
+        }
+        
+    except Exception as e:
+        if 'temp_path' in locals() and os.path.exists(temp_path):
+            os.unlink(temp_path)
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Gradio interface
+def gradio_predict(input_data):
+    """Handle both direct text and file uploads"""
+    try:
+        if isinstance(input_data, str):  # Direct text input
+            text = input_data
+        else:  # File upload
+            temp_path = save_upload_file(input_data)
+            if temp_path.endswith('.json'):
+                with open(temp_path, 'r') as f:
+                    data = json.load(f)
+                text = data.get('description', '')
+            else:
+                with open(temp_path, 'r') as f:
+                    text = f.read()
+            os.unlink(temp_path)
+        
+        if not text.strip():
+            return {"error": "No text content found"}
+        
+        result = emotion_analysis(text)
+        return {
+            "emotions": [
+                {e['label']: float(e['score'])} 
+                for e in sorted(result[0], key=lambda x: x['score'], reverse=True)
+            ]
+        }
+    
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=gradio_predict,
+    inputs=[
+        gr.Textbox(label="Enter text directly", lines=5),
+        gr.File(label="Or upload text/JSON file", file_types=[".txt", ".json"])
+    ],
+    outputs=gr.JSON(label="Emotion Analysis"),
+    title="Text Emotion Analysis",
+    description="Analyze emotion in text using RoBERTa model",
+    examples=[
+        ["I'm feeling absolutely thrilled about this new project!"],
+        ["This situation is making me extremely anxious and worried."]
+    ]
+)
+
+# Mount Gradio app
+app = gr.mount_gradio_app(app, demo, path="/")
+
+# For running locally
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+torch
+gradio
+fastapi
+uvicorn
+python-multipart