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@@ -1,14 +1,13 @@
-# FROM python:3.9-slim
-# WORKDIR /app
-# COPY . .
-# COPY requirements.txt .
-# COPY app.py .   
-# RUN pip install --no-cache-dir --upgrade -r requirements.txt
-# CMD ["streamlit", "run", "app.py", "server.address=0.0.0.0", "server.port=8501"]
-FROM python:3.9-slim
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-COPY app.py .
-CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
-
+# FROM python:3.9-slim
+# WORKDIR /app
+# COPY . .
+# COPY requirements.txt .
+# COPY app.py .   
+# RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# CMD ["streamlit", "run", "app.py", "server.address=0.0.0.0", "server.port=8501"]
+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY app.py .
+CMD ["streamlit", "run", "app.py", "--server.port=7862", "--server.address=0.0.0.0"]

app.py

@@ -46,20 +46,68 @@ customer_data = pd.DataFrame([{
 
 
 # Single prediction section
+# if st.button("Predict"):
+#     try:
+#         print(customer_data)
+#         response = requests.post(
+#             "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
+#             json=customer_data.to_dict(orient='records')[0]
+#         )  # Send data to Flask API
+#         response.raise_for_status()  # Raise an error if the request fails
+#         result = response.json()
+#         churn_prediction = result["Prediction"]  # Extract only the value
+#         st.write(f"Based on the information provided, the customer with ID {CustomerID} is likely to {churn_prediction}.")
+#     except requests.exceptions.RequestException as e:
+#         st.error(f"Error making prediction: {e}")
+        
+
+
+# Function for handling retries with exponential backoff
+def make_request_with_backoff(url, payload, max_retries=5):
+    retry_delay = 2  # Initial retry delay (in seconds)
+
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(url, json=payload)
+            response.raise_for_status()  # Ensure successful response
+
+            return response.json()  # Return data if request succeeds
+
+        except requests.exceptions.RequestException as e:
+            if response.status_code == 429:  # Too many requests
+                st.warning(f"Rate limit exceeded. Retrying in {retry_delay} seconds...")
+                time.sleep(retry_delay)  # Wait before retrying
+                retry_delay *= 2  # Exponential backoff (2s, 4s, 8s, etc.)
+            else:
+                st.error(f"Request failed: {e}")
+                return None
+
+    st.error("Max retries reached. Try again later.")
+    return None
+
+# Single prediction section with retry mechanism
 if st.button("Predict"):
     try:
-        print(customer_data)
-        response = requests.post(
+        print(customer_data)  # Display input data for debugging
+        
+        result = make_request_with_backoff(
             "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
-            json=customer_data.to_dict(orient='records')[0]
-        )  # Send data to Flask API
-        response.raise_for_status()  # Raise an error if the request fails
-        result = response.json()
-        churn_prediction = result["Prediction"]  # Extract only the value
-        st.write(f"Based on the information provided, the customer with ID {CustomerID} is likely to {churn_prediction}.")
-    except requests.exceptions.RequestException as e:
-        st.error(f"Error making prediction: {e}")
+            customer_data.to_dict(orient='records')[0]
+        )
+
+        if result:
+            churn_prediction = result.get("Prediction", "Unknown")  # Safe key lookup
+            customer_id = customer_data.get("CustomerID", "Unknown")  # Prevent undefined errors
+            st.write(f"Based on the information provided, the customer with ID {customer_id} is likely to {churn_prediction}.")
         
+    except KeyError as e:
+        st.error(f"Unexpected response format: Missing key {e}")
+    except Exception as e:
+        st.error(f"Unexpected error: {e}")
+
+
+
+
 
 # Batch Prediction
 st.subheader("Batch Prediction")

requirements.txt

@@ -1,3 +1,3 @@
-pandas==2.2.2
-requests==2.28.1
+pandas==2.2.2
+requests==2.28.1
 streamlit==1.43.2