Update app.py

app.py

@@ -90,9 +90,9 @@ def main():
     
     candidate_docs = []
     if uploaded_file is not None:
-        # Check if the file size exceeds 3MB (3 * 1024 * 1024 bytes)
-        if uploaded_file.size > 3 * 1024 * 1024:
-            st.error("The file is too large! Please upload a file smaller than 3MB.")
+        # Check if the file size exceeds 1MB (1 * 1024 * 1024 bytes)
+        if uploaded_file.size > 1 * 1024 * 1024:
+            st.error("The file is too large! Please upload a file smaller than 1MB.")
         else:
             try:
                 df = pd.read_csv(uploaded_file)
@@ -234,7 +234,7 @@ def main():
             progress_bar.progress(75)
             
             import random
-            max_tweets = 500
+            max_tweets = 1000
             if len(scored_docs) > max_tweets:
                 sampled_docs = random.sample(scored_docs, max_tweets)
                 st.info(f"Sampling {max_tweets} out of {len(scored_docs)} tweets for report generation")
@@ -244,9 +244,10 @@ def main():
             # Build prompt
             messages = [
                 {"role": "user", "content": f"""
-Generate a well-structured business report based on tweets from twitter/X with sentiment score (0: negative, 1: neutral, 2: positive) that answers Query Question and meets following Requirements. 
+Generate a well-structured business report based on tweets from twitter/X with sentiment score (0: negative, 1: neutral, 2: positive) that answers Query Question and meets following Requirements: 
 **Requirements:**
-- Include an introduction, key insights, and a conclusion.
+- Do not include any greeting, summary statement, or closing question.
+- Generate only the final report text, starting with the introduction, key insights, and ending with the conclusion.
 - Ensure the analysis is concise and does not cut off abruptly. 
 - Summarize major findings without repeating verbatim.
 - Cover both positive and negative aspects, highlighting trends in user sentiment.