Added supportive post request code

app.py

@@ -1,14 +1,68 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# import streamlit as st
+# import torch
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Load the model and tokenizer
+# # 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 @@ 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():
@@ -25,27 +80,54 @@ def predict_news(text, tokenizer, model):
     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)
+
+if __name__ == "__main__":
+    fastapi_thread = Thread(target=start_fastapi, daemon=True)
+    fastapi_thread.start()
+
+    # Start Streamlit
+    cli.main()