Create app.py

app.py

@@ -0,0 +1,81 @@
+# Install 
+import gradio as gr
+import google.generativeai as genai
+import json
+
+# ✅ Configure Gemini API key
+API_KEY = "AIzaSyAtm1yxPoXsz30KJUnyQNN9QeGw3FMIoMU"
+genai.configure(api_key=API_KEY)
+model = genai.GenerativeModel(model_name="gemini-2.0-flash")
+
+# ✅ Analyze relationship chat from text input
+def analyze_text(text):
+    prompt = f"""
+You are a relationship AI assistant.
+
+Analyze the chat text below and do all of the following:
+
+1. Provide a brief sentiment/emotional summary.
+2. Identify key events or milestones in the relationship.
+3. Write a short, heartfelt relationship narrative.
+4. Calculate a compatibility score from 0 to 100.
+5. Provide a clear and friendly interpretation of that score.
+
+Return ONLY a JSON object with the following keys:
+"sentiment_summary", "key_events", "narrative", "compatibility_score", "interpretation"
+
+Chat text:
+\"\"\"{text}\"\"\"
+"""
+    response = model.generate_content(prompt)
+    return response.text.strip()
+
+# ✅ Process and return outputs
+def process_chat(chat_text):
+    if not chat_text.strip():
+        return "", "", "", "", "", ""
+
+    analysis = analyze_text(chat_text)
+
+    try:
+        parsed = json.loads(analysis)
+    except Exception:
+        parsed = None
+
+    if parsed:
+        sentiment = parsed.get("sentiment_summary", "")
+        events = parsed.get("key_events", "")
+        narrative = parsed.get("narrative", "")
+        score = str(parsed.get("compatibility_score", ""))
+        interpretation = parsed.get("interpretation", "")
+    else:
+        sentiment = events = narrative = score = interpretation = ""
+
+    return analysis, sentiment, events, narrative, score, interpretation
+
+# ✅ Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 💬 Relationship Chat Text Analyzer with Gemini 2.0 Flash")
+
+    chat_input = gr.Textbox(label="Paste your chat here", lines=20, placeholder="Paste chat content here...")
+
+    raw_output = gr.Textbox(label="Raw AI Analysis Response", lines=10, interactive=False)
+    sentiment_output = gr.Textbox(label="Sentiment Summary", interactive=False)
+    events_output = gr.Textbox(label="Key Events / Milestones", interactive=False)
+    narrative_output = gr.Textbox(label="Relationship Narrative", interactive=False)
+    score_output = gr.Textbox(label="Compatibility Score", interactive=False)
+    interpretation_output = gr.Textbox(label="Interpretation", interactive=False)
+
+    analyze_btn = gr.Button("Analyze Relationship")
+
+    analyze_btn.click(process_chat, inputs=chat_input, outputs=[
+        raw_output,
+        sentiment_output,
+        events_output,
+        narrative_output,
+        score_output,
+        interpretation_output,
+    ])
+
+# ✅ Launch app
+demo.launch()