Update main.py

main.py

@@ -17,6 +17,7 @@ import google.generativeai as genai
 import uuid
 import base64
 from io import BytesIO
+import requests # Added for making API calls
 
 load_dotenv()
 
@@ -62,6 +63,7 @@ class FlaskResponse(ResponseParser):
 
 
 gemini_api_key = os.getenv('Gemini')
+# --- Model name reverted to your original specification ---
 llm = ChatGoogleGenerativeAI(api_key=gemini_api_key, model='gemini-2.0-flash-thinking-exp', temperature=0.1)
 
 gemini_api_key = os.environ['Gemini']
@@ -74,6 +76,7 @@ generation_config = {
   "max_output_tokens": 5000,
 }
 
+# --- Model name reverted to your original specification ---
 model = genai.GenerativeModel(
   model_name="gemini-2.0-flash-lite-001",
   generation_config=generation_config,
@@ -85,63 +88,95 @@ new_filename = f"{guid}"
 user_defined_path = os.path.join("/exports/charts", new_filename)
 
 
-# Endpoint for chat
+# --- REFACTORED Endpoint for chat ---
 @app.route("/chat", methods=["POST"])
 @cross_origin()
 def bot():
     # Retrieve parameters from the request
-    json_table = request.json.get("json_table")
+    profile_id = request.json.get("profile_id")
     user_question = request.json.get("user_question")
-    print("User question:", user_question)
-
-    # Convert the table data into a dataframe
-    data = eval(str(json_table))
-    df = pd.DataFrame(data)
-    print("Columns in dataframe:", list(df.columns))
-
-    # Create a SmartDataframe instance using your configuration.
-    pandas_agent = SmartDataframe(
-        df,
-        config={
-            "llm": llm,
-            "response_parser": FlaskResponse,
-            "custom_whitelisted_dependencies": [
-                "os",
-                "io",
-                "sys",
-                "chr",
-                "glob",
-                "b64decoder",
-                "collections",
-                "geopy",
-                "geopandas",
-                "wordcloud",
-                "builtins"
-            ],
-            "security": "none", "save_charts_path": user_defined_path, "save_charts": False, "enable_cache": False, "conversational":True
-        }
-    )
-
-    # Get the answer from the agent
-    answer = pandas_agent.chat(user_question)
-
-    # Process the answer based on its type
-    formatted_answer = None
-    if isinstance(answer, pd.DataFrame):
-        formatted_answer = answer.to_html()
-    elif isinstance(answer, plt.Figure):
-        buf = io.BytesIO()
-        answer.savefig(buf, format="png")
-        buf.seek(0)
-        image_base64 = base64.b64encode(buf.read()).decode("utf-8")
-        formatted_answer = f"data:image/png;base64,{image_base64}"
-    elif isinstance(answer, (int, float)):
-        formatted_answer = str(answer)
-    else:
-        formatted_answer = str(answer)
-
-    # Return the formatted answer as JSON.
-    return jsonify({"answer": formatted_answer})
+
+    if not profile_id or not user_question:
+        return jsonify({"error": "Missing 'profile_id' or 'user_question' in request."}), 400
+
+    print(f"Received request for profile_id: {profile_id}")
+    print(f"User question: {user_question}")
+
+    # Fetch data from the external API
+    API_URL = "https://irisplustech.com/public/api4/business/profile/user/get-recent-transactions-v2"
+    payload = {'profile_id': profile_id}
+
+    try:
+        response = requests.post(API_URL, data=payload)
+        # Check if the request was successful
+        if response.status_code != 200:
+            return jsonify({
+                "error": "Failed to fetch data from the transaction API.",
+                "status_code": response.status_code,
+                "details": response.text
+            }), 502 # Bad Gateway
+
+        api_data = response.json()
+        
+        # Check for API-level errors
+        if api_data.get("error"):
+            return jsonify({
+                "error": "Transaction API returned an error.",
+                "message": api_data.get("message", "No message provided.")
+            }), 400
+
+        transactions = api_data.get("transactions")
+        if transactions is None or not isinstance(transactions, list):
+             return jsonify({"error": "Invalid data format from transaction API. 'transactions' key is missing or not a list."}), 500
+        
+        if not transactions:
+            return jsonify({"answer": "No transaction data was found for this profile. I can't answer any questions."})
+
+        # Convert the transaction data into a dataframe
+        df = pd.DataFrame(transactions)
+        print("Columns in dataframe:", list(df.columns))
+
+        # Create a SmartDataframe instance using your configuration.
+        pandas_agent = SmartDataframe(
+            df,
+            config={
+                "llm": llm,
+                "response_parser": FlaskResponse,
+                "custom_whitelisted_dependencies": [
+                    "os", "io", "sys", "chr", "glob",
+                    "b64decoder", "collections", "geopy",
+                    "geopandas", "wordcloud", "builtins"
+                ],
+                "security": "none", "save_charts_path": user_defined_path, 
+                "save_charts": False, "enable_cache": False, "conversational":True
+            }
+        )
+
+        # Get the answer from the agent
+        answer = pandas_agent.chat(user_question)
+
+        # Process the answer based on its type
+        formatted_answer = None
+        if isinstance(answer, pd.DataFrame):
+            formatted_answer = answer.to_html()
+        elif isinstance(answer, plt.Figure):
+            buf = io.BytesIO()
+            answer.savefig(buf, format="png")
+            buf.seek(0)
+            image_base64 = base64.b64encode(buf.read()).decode("utf-8")
+            formatted_answer = f"data:image/png;base64,{image_base64}"
+        else:
+            formatted_answer = str(answer)
+        
+        # Return the formatted answer as JSON.
+        return jsonify({"answer": formatted_answer})
+
+    except requests.exceptions.RequestException as e:
+        return jsonify({"error": "Could not connect to the transaction API.", "details": str(e)}), 500
+    except Exception as e:
+        logging.exception("An unexpected error occurred in /chat endpoint")
+        return jsonify({"error": "An unexpected server error occurred.", "details": str(e)}), 500
+
 
 # Reports endpoint
 @app.route("/report", methods=["POST"])
@@ -178,22 +213,7 @@ You are an Quantilytix Marketing Specialist. Analyze the following data and gene
 
     return jsonify(str(report))
 
-# Business Plan
-@app.route("/bplan", methods=["POST"])
-@cross_origin()
-def business_plan():
-    json_data = request.json.get("json_data")
-
-    prompt = """
-You are Quantilytix business analyst. Analyze the following data and generate a comprehensive business plan to help the business look for funding and support. Use markdown formatting and tables where necessary. only return the repor and nothing else, the data:
-"""  +  str(json_data)
-
-    
-    response = model.generate_content(prompt)
-    report = response.text
-
-
-    return jsonify(str(report))
+# Business Plan endpoint REMOVED
     
 #Notificatiions
 @app.route("/notify", methods=["POST"])