Added api endpoint 1

app.py

@@ -1,14 +1,68 @@
-# import streamlit as st
-# import torch
-# from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# # Load the model and tokenizer
+# # import streamlit as st
+# # import torch
+# # from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# # # Load the model and tokenizer
+# # # @st.cache_resource
+# # # def load_model():
+# # #     tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+# # #     model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
+# # #     model.eval()
+# # #     return tokenizer, model
 # # @st.cache_resource
 # # def load_model():
-# #     tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+# #     tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
 # #     model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
 # #     model.eval()
 # #     return tokenizer, model
+
+# # def predict_news(text, tokenizer, model):
+# #     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+# #     with torch.no_grad():
+# #         outputs = model(**inputs)
+# #     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# #     predicted_label = torch.argmax(probabilities, dim=-1).item()
+# #     confidence = probabilities[0][predicted_label].item()
+# #     return "FAKE" if predicted_label == 1 else "REAL", confidence
+
+# # def main():
+# #     st.title("News Classifier")
+    
+# #     # Load model
+# #     tokenizer, model = load_model()
+    
+# #     # Text input
+# #     news_text = st.text_area("Enter news text to analyze:", height=200)
+    
+# #     if st.button("Classify"):
+# #         if news_text:
+# #             with st.spinner('Analyzing...'):
+# #                 prediction, confidence = predict_news(news_text, tokenizer, model)
+                
+# #                 # Display results
+# #                 if prediction == "FAKE":
+# #                     st.error(f"⚠️ {prediction} NEWS")
+# #                 else:
+# #                     st.success(f"✅ {prediction} NEWS")
+                
+# #                 st.info(f"Confidence: {confidence*100:.2f}%")
+
+# # if __name__ == "__main__":
+# #     main()
+
+
+# import streamlit as st
+# import torch
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# from fastapi import FastAPI, Request
+# from pydantic import BaseModel
+# from threading import Thread
+# from streamlit.web import cli
+
+# # FastAPI app
+# api_app = FastAPI()
+
+# # Load the model and tokenizer
 # @st.cache_resource
 # def load_model():
 #     tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
@@ -16,6 +70,7 @@
 #     model.eval()
 #     return tokenizer, model
 
+# # Prediction function
 # def predict_news(text, tokenizer, model):
 #     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
 #     with torch.no_grad():
@@ -25,109 +80,94 @@
 #     confidence = probabilities[0][predicted_label].item()
 #     return "FAKE" if predicted_label == 1 else "REAL", confidence
 
-# def main():
-#     st.title("News Classifier")
-    
-#     # Load model
+# # FastAPI request model
+# class NewsInput(BaseModel):
+#     text: str
+
+# # FastAPI route for POST requests
+# @api_app.post("/classify")
+# async def classify_news(data: NewsInput):
 #     tokenizer, model = load_model()
-    
-#     # Text input
-#     news_text = st.text_area("Enter news text to analyze:", height=200)
-    
-#     if st.button("Classify"):
-#         if news_text:
-#             with st.spinner('Analyzing...'):
-#                 prediction, confidence = predict_news(news_text, tokenizer, model)
-                
-#                 # Display results
-#                 if prediction == "FAKE":
-#                     st.error(f"⚠️ {prediction} NEWS")
-#                 else:
-#                     st.success(f"✅ {prediction} NEWS")
-                
-#                 st.info(f"Confidence: {confidence*100:.2f}%")
+#     prediction, confidence = predict_news(data.text, tokenizer, model)
+#     return {
+#         "prediction": prediction,
+#         "confidence": f"{confidence*100:.2f}%"
+#     }
+
+# # Streamlit app
+# def run_streamlit():
+#     def main():
+#         st.title("News Classifier")
+        
+#         # Load model
+#         tokenizer, model = load_model()
+        
+#         # Text input
+#         news_text = st.text_area("Enter news text to analyze:", height=200)
+        
+#         if st.button("Classify"):
+#             if news_text:
+#                 with st.spinner('Analyzing...'):
+#                     prediction, confidence = predict_news(news_text, tokenizer, model)
+                    
+#                     # Display results
+#                     if prediction == "FAKE":
+#                         st.error(f"⚠️ {prediction} NEWS")
+#                     else:
+#                         st.success(f"✅ {prediction} NEWS")
+                    
+#                     st.info(f"Confidence: {confidence*100:.2f}%")
 
-# if __name__ == "__main__":
 #     main()
 
+# # Threaded execution for FastAPI and Streamlit
+# def start_fastapi():
+#     import uvicorn
+#     uvicorn.run(api_app, host="0.0.0.0", port=8502)
 
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from fastapi import FastAPI, Request
+# if __name__ == "__main__":
+#     fastapi_thread = Thread(target=start_fastapi, daemon=True)
+#     fastapi_thread.start()
+
+#     # Start Streamlit
+#     cli.main()
+
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-from threading import Thread
-from streamlit.web import cli
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-# FastAPI app
-api_app = FastAPI()
+# Define the FastAPI app
+app = FastAPI()
 
-# Load the model and tokenizer
-@st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
-    model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
-    model.eval()
-    return tokenizer, model
+# Define the input data schema
+class InputText(BaseModel):
+    text: str
+
+# Load the model and tokenizer (ensure these paths are correct in your Space)
+tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
+model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
+model.eval()
 
 # Prediction function
-def predict_news(text, tokenizer, model):
+def predict_news(text: str):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
     with torch.no_grad():
         outputs = model(**inputs)
     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
     predicted_label = torch.argmax(probabilities, dim=-1).item()
     confidence = probabilities[0][predicted_label].item()
-    return "FAKE" if predicted_label == 1 else "REAL", confidence
-
-# FastAPI request model
-class NewsInput(BaseModel):
-    text: str
-
-# FastAPI route for POST requests
-@api_app.post("/classify")
-async def classify_news(data: NewsInput):
-    tokenizer, model = load_model()
-    prediction, confidence = predict_news(data.text, tokenizer, model)
     return {
-        "prediction": prediction,
-        "confidence": f"{confidence*100:.2f}%"
+        "prediction": "FAKE" if predicted_label == 1 else "REAL",
+        "confidence": round(confidence * 100, 2)  # Return confidence as a percentage
     }
 
-# Streamlit app
-def run_streamlit():
-    def main():
-        st.title("News Classifier")
-        
-        # Load model
-        tokenizer, model = load_model()
-        
-        # Text input
-        news_text = st.text_area("Enter news text to analyze:", height=200)
-        
-        if st.button("Classify"):
-            if news_text:
-                with st.spinner('Analyzing...'):
-                    prediction, confidence = predict_news(news_text, tokenizer, model)
-                    
-                    # Display results
-                    if prediction == "FAKE":
-                        st.error(f"⚠️ {prediction} NEWS")
-                    else:
-                        st.success(f"✅ {prediction} NEWS")
-                    
-                    st.info(f"Confidence: {confidence*100:.2f}%")
-
-    main()
-
-# Threaded execution for FastAPI and Streamlit
-def start_fastapi():
-    import uvicorn
-    uvicorn.run(api_app, host="0.0.0.0", port=8502)
-
-if __name__ == "__main__":
-    fastapi_thread = Thread(target=start_fastapi, daemon=True)
-    fastapi_thread.start()
+# Define the POST endpoint
+@app.post("/predict")
+async def classify_news(input_text: InputText):
+    try:
+        result = predict_news(input_text.text)
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
-    # Start Streamlit
-    cli.main()