Moved app.py to the root for Hugging Face deployment

app.py

@@ -0,0 +1,43 @@
+from vocca_ai.ai_response import generate_call_summary
+from vocca_ai.intent_classifier import classify_intent
+from vocca_ai.sentiment import analyze_sentiment
+from vocca_ai.db_handler import log_call, fetch_recent_calls
+import streamlit as st
+from vocca_ai.preprocess import priority_score
+from vocca_ai.intent_classifier import classify_intent  # Now uses MiniLM
+
+import sys
+import os
+
+# Ensure Python can find the 'models' directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+st.title("🩺 AI-Powered Call Insights for Vocca")
+st.write("Analyze patient calls, detect urgency, and generate AI-powered responses.")
+
+user_input = st.text_area("📞 Enter Call Transcript:", height=150)
+
+if user_input:
+    intent = classify_intent(user_input)
+    priority = priority_score(user_input)
+    sentiment = analyze_sentiment(user_input)  # Now using DistilBERT
+    ai_response = generate_call_summary(user_input)  # Now using Falcon-7B
+
+    st.subheader("📌 Extracted Call Insights")
+    st.write(f"**Intent:** {intent}")
+    st.write(f"**Priority Level:** {priority}")
+    st.write(f"**Sentiment:** {sentiment}")
+    st.write(f"**AI Suggested Response:** {ai_response}")
+
+    log_call(user_input, intent, priority, sentiment, ai_response)
+
+    st.success("✅ Call successfully logged & analyzed!")
+
+if st.button("📊 Show Recent Calls"):
+    calls = fetch_recent_calls()
+    st.subheader("📊 Recent Call Logs")
+    for row in calls:
+        st.write(f"📞 **Transcript:** {row[1]}")
+        st.write(f"🔍 **Intent:** {row[2]}, **Priority:** {row[3]}, **Sentiment:** {row[4]}")
+        st.write(f"🤖 **AI Response:** {row[5]}")
+        st.write("---")