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KrSharangrav
commited on
Commit
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4ec2156
1
Parent(s):
f89cec9
changed chatbot
Browse files- chatbot.py +4 -4
chatbot.py
CHANGED
@@ -82,21 +82,21 @@ def chatbot_response(user_prompt):
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entry_user = entry.get("user", "Unknown")
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entry_date = entry.get("date", "Unknown")
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-
# Build a static response message with
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ai_response = (
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"Let's break down this tweet-like MongoDB entry:\n\n"
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-
f"
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f"User: {entry_user}\n"
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f"Date: {entry_date}"
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)
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#
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sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
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topic_label, topic_confidence = extract_topic(entry_text)
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return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
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else:
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# For other queries, use the generative model flow.
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model_gen = genai.GenerativeModel("gemini-1.5-pro")
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ai_response_obj = model_gen.generate_content(user_prompt)
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ai_response = ai_response_obj.text
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entry_user = entry.get("user", "Unknown")
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entry_date = entry.get("date", "Unknown")
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+
# Build a static response message with the desired formatting.
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ai_response = (
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"Let's break down this tweet-like MongoDB entry:\n\n"
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+
f"Tweet: {entry_text}\n"
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f"User: {entry_user}\n"
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f"Date: {entry_date}"
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)
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+
# Run sentiment and topic analysis on the entry's text.
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sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
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topic_label, topic_confidence = extract_topic(entry_text)
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return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
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else:
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+
# For all other queries, use the generative model flow.
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model_gen = genai.GenerativeModel("gemini-1.5-pro")
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ai_response_obj = model_gen.generate_content(user_prompt)
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ai_response = ai_response_obj.text
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