Update main.py

main.py

@@ -1,45 +1,45 @@
-from fastapi import FastAPI, Query, Request, HTTPException
-import pandas as pd
-import transformers as pipeline 
-from transformers import AutoTokenizer,AutoModelForSequenceClassification
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+# from fastapi import FastAPI, Query, Request, HTTPException
+# import pandas as pd
+# import transformers as pipeline 
+# from transformers import AutoTokenizer,AutoModelForSequenceClassification
+# 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()
+# model_name = "Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets"
+# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-@app.get("/")
-async def read_root():
-    return {"message": "Sentiment Analysis API using FastAPI"}
+# sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-@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 = FastAPI()
 
-    if sentiment_label == 'LABEL_1':
-        sentiment_label = "positive"
-    elif sentiment_label == 'LABEL_0':
-        sentiment_label = "neutral"
-    else:
-        sentiment_label = "negative"
+# @app.get("/")
+# async def read_root():
+#     return {"message": "Sentiment Analysis API using FastAPI"}
+
+# @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']
+
+#     if sentiment_label == 'LABEL_1':
+#         sentiment_label = "positive"
+#     elif sentiment_label == 'LABEL_0':
+#         sentiment_label = "neutral"
+#     else:
+#         sentiment_label = "negative"
 
-    response = {
-        "sentiment": sentiment_label.capitalize(),
-        "score": sentiment_score
-    }
+#     response = {
+#         "sentiment": sentiment_label.capitalize(),
+#         "score": sentiment_score
+#     }
 
-    return response
+#     return response
 
-if _name_ == "_main_":
-    import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=7860)
+# if _name_ == "_main_":
+#     import uvicorn
+#     uvicorn.run(app, host="127.0.0.1", port=7860)
 
 
 # model_name = "Sonny4Sonnix/Movie_Sentiments_Analysis_with_FastAPI"  # Replace with the name of the pre-trained model you want to use