try2

app.py

@@ -1,68 +1,14 @@
-# # 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', 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')
+#     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', use_fast=False)
@@ -70,7 +16,6 @@ def load_model():
     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():
@@ -80,104 +25,159 @@ def predict_news(text, tokenizer, model):
     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):
+def main():
+    st.title("News Classifier")
+    
+    # Load model
     tokenizer, model = load_model()
-    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}%")
-
-    main()
-
-# Threaded execution for FastAPI and Streamlit
-def start_fastapi():
-    import uvicorn
-    uvicorn.run(api_app, host="0.0.0.0", port=8502)
+    
+    # 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__":
-    fastapi_thread = Thread(target=start_fastapi, daemon=True)
-    fastapi_thread.start()
+    main()
 
-    # Start Streamlit
-    cli.main()
 
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# 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
 
-# from fastapi.middleware.cors import CORSMiddleware
-
-
-# # Define the FastAPI app
-# app = FastAPI()
-
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=["*"],  # Update with your frontend's URL for security
-#     allow_credentials=True,
-#     allow_methods=["*"],
-#     allow_headers=["*"],
-# )
-# # Define the input data schema
-# class InputText(BaseModel):
-#     text: str
+# # FastAPI app
+# api_app = FastAPI()
 
-# # 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()
+# # 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
 
 # # Prediction function
-# def predict_news(text: str):
+# 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
+
+# # 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": "FAKE" if predicted_label == 1 else "REAL",
-#         "confidence": round(confidence * 100, 2)  # Return confidence as a percentage
+#         "prediction": prediction,
+#         "confidence": f"{confidence*100:.2f}%"
 #     }
 
-# # 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))
+# # 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()
+
+#     # Start Streamlit
+#     cli.main()
+
+# # from fastapi import FastAPI, HTTPException
+# # from pydantic import BaseModel
+# # from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# # import torch
+
+# # from fastapi.middleware.cors import CORSMiddleware
+
+
+# # # Define the FastAPI app
+# # app = FastAPI()
+
+# # app.add_middleware(
+# #     CORSMiddleware,
+# #     allow_origins=["*"],  # Update with your frontend's URL for security
+# #     allow_credentials=True,
+# #     allow_methods=["*"],
+# #     allow_headers=["*"],
+# # )
+# # # 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: 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 {
+# #         "prediction": "FAKE" if predicted_label == 1 else "REAL",
+# #         "confidence": round(confidence * 100, 2)  # Return confidence as a percentage
+# #     }
+
+# # # 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))