Create app.py

app.py

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+from transformers import pipeline
+
+# Step 1: Define the sentiment analysis pipeline.
+# Here, we use a text classification model.
+sentiment_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1")
+
+# Step 2: Define the text generation pipeline with Gemma.
+gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it")
+
+# Example input text that we want to analyze.
+text = "I love this new product. It has exceeded my expectations and I feel very happy about it."
+
+# Perform sentiment analysis on the text.
+sentiment_result = sentiment_pipe(text)
+print("Sentiment Analysis Result:")
+print(sentiment_result)
+
+# Prepare a prompt that includes the original text and the sentiment result.
+# The prompt instructs the Gemma model to generate a detailed summary report.
+prompt = f"""
+Generate a detailed report based on the following analysis.
+
+Original text:
+"{text}"
+
+Sentiment analysis result:
+{sentiment_result}
+
+Please provide a concise summary report explaining the sentiment and key insights.
+"""
+
+# Use the Gemma pipeline to generate the summary report.
+report = gemma_pipe(prompt, max_length=200)
+print("\nGenerated Report:")
+print(report[0]['generated_text'])