Update main.py

main.py

@@ -3,53 +3,95 @@ import pandas as pd
 import transformers as pipeline 
 from transformers import AutoTokenizer,AutoModelForSequenceClassification
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-import os
-#os.environ["TRANSFORMERS_CACHE"] = "/path/to/writable/cache/directory"
+# import os
+# #os.environ["TRANSFORMERS_CACHE"] = "/path/to/writable/cache/directory"
 
 
-model_name = "Sonny4Sonnix/Movie_Sentiments_Analysis_with_FastAPI"  # Replace with the name of the pre-trained model you want to use
+from fastapi import FastAPI, HTTPException, Query
+
+import transformers
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+model_name = "Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets"
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
 app = FastAPI()
 
 @app.get("/")
 async def read_root():
-    return {"message": "Welcome to the Sepsis Prediction using FastAPI"}
+    return {"message": "Sentiment Analysis API using FastAPI"}
 
-def classify(prediction):
-    if prediction == 0:
-        return "Sentence is positive"
-    else:
-        return "Sentence is negative"
+@app.get("/analyze-sentiment/")
+async def analyze_sentiment(text: str = Query(..., description="Text for sentiment analysis")):
+    result = sentiment(text)
+    sentiment_label = result[0]['label']
+    sentiment_score = result[0]['score']
 
-        
-@app.post("/predict/")
-async def predict_sepsis(
-    request: Request,
-    Text: float = Query(..., description="Please type a sentence"),
-):
-    
-    input_data = [Text]
-
-    input_df = pd.DataFrame([input_data], columns=[
-        "Text"
-    ])
-
-    pred = model.predict(input_df)
-    output = classify(pred[0])
+    if sentiment_label == 'LABEL_1':
+        sentiment_label = "positive"
+    elif sentiment_label == 'LABEL_0':
+        sentiment_label = "neutral"
+    else:
+        sentiment_label = "negative"
 
     response = {
-        "prediction": output
+        "sentiment": sentiment_label.capitalize(),
+        "score": sentiment_score
     }
 
     return response
 
-    # Run the app using Uvicorn
-if __name__ == "__main__":
+if _name_ == "_main_":
     import uvicorn
     uvicorn.run(app, host="127.0.0.1", port=7860)
-sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+
+# model_name = "Sonny4Sonnix/Movie_Sentiments_Analysis_with_FastAPI"  # Replace with the name of the pre-trained model you want to use
+# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# app = FastAPI()
+
+# @app.get("/")
+# async def read_root():
+#     return {"message": "Welcome to the Sepsis Prediction using FastAPI"}
+
+# def classify(prediction):
+#     if prediction == 0:
+#         return "Sentence is positive"
+#     else:
+#         return "Sentence is negative"
+
+        
+# @app.post("/predict/")
+# async def predict_sepsis(
+#     request: Request,
+#     Text: float = Query(..., description="Please type a sentence"),
+# ):
+    
+#     input_data = [Text]
+
+#     input_df = pd.DataFrame([input_data], columns=[
+#         "Text"
+#     ])
+
+#     pred = model.predict(input_df)
+#     output = classify(pred[0])
+
+#     response = {
+#         "prediction": output
+#     }
+
+#     return response
+
+#     # Run the app using Uvicorn
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run(app, host="127.0.0.1", port=7860)
+# sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)