Upload app.py

app.py

@@ -0,0 +1,68 @@
+from flask import Flask, request, jsonify, render_template,send_from_directory
+from flask_cors import CORS
+import logging
+from query_ai import query_gemini
+from knowledge_base import search_knowledge_base, save_to_knowledge_base
+from prompts import get_prompt
+import os
+# from speech_to_text import recognize_speech
+
+# Initialize Flask app
+app = Flask(__name__, static_folder="Static")
+CORS(app, resources={r"/*": {"origins": "*"}})
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+
+@app.route("/")
+def home():
+    # return "Chatbot is running"
+    return render_template("index.html")
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        data = request.json
+        user_input = data.get("message", "").strip()
+        query_type = data.get("format", "default")
+
+        if not user_input:
+            return jsonify({"response": "Please provide a valid message."})
+
+        # 🔹 Step 1: Check predefined prompts
+        prompt_response = get_prompt(user_input.lower()) or "Let me find an answer for you."
+
+        # 🔹 Step 2: Search the knowledge base
+        kb_response = search_knowledge_base(user_input)
+
+        # 🔹 Step 3: Construct final prompt
+        final_prompt = f"{prompt_response}\n\nUser Query: {user_input}"
+        if kb_response:
+            final_prompt = f"{prompt_response}\n\nBased on the knowledge base: {kb_response}\n\nUser Query: {user_input}"
+
+        # 🔹 Step 4: Generate AI response
+        response = query_gemini(final_prompt, query_type=query_type)
+
+        # 🔹 Step 5: Save response if it's new
+        if not kb_response:
+            save_to_knowledge_base(user_input, response)
+
+        return jsonify({"response": response})
+
+    except Exception as e:
+        logging.error(f"Error in /chat endpoint: {e}")
+        return jsonify({"response": "An error occurred while processing your request."})
+
+# @app.route("/script/chatbot.js")
+# def chatbot_script():
+#     return send_from_directory("static", "chatbot.js")
+
+@app.route('/speech', methods=['GET'])
+def speech_to_text():
+    """Endpoint to recognize speech and return text."""
+    text = recognize_speech()
+    return jsonify({"recognized_text": text})
+
+if __name__ == '__main__':
+    # port = int(os.environ.get("PORT", 10000))  # Use Render's default port
+    app.run(host="0.0.0.0", debug=True)