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@@ -1,21 +1,16 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,47 @@
+
+import streamlit as st
+import requests
+
+st.title("Lead Prediction System")
+
+# Input fields
+age = st.number_input("Age", min_value=0, value=25)
+current_occupation = st.selectbox("Current Occupation", ["Professional", "Unemployed", "Student"])
+first_interaction = st.selectbox("First Interaction Platform", ["Website", "Mobile App"])
+profile_completed = st.selectbox("Profile Completion Level", ["Low", "Medium", "High"])
+website_visits = st.number_input("Number of Website Visits", min_value=0, value=3)
+time_spent_on_website = st.number_input("Time Spent on Website (in seconds)", min_value=0.0, value=180.0)
+page_views_per_visit = st.number_input("Page Views per Visit", min_value=0.0, value=4.5)
+last_activity = st.selectbox("Last Activity Type", ["Email Activity", "Phone Activity", "Website Activity"])
+print_media_type1 = st.selectbox("Seen Newspaper Ad?", ["Yes", "No"])
+print_media_type2 = st.selectbox("Seen Magazine Ad?", ["Yes", "No"])
+digital_media = st.selectbox("Seen Digital Media Ad?", ["Yes", "No"])
+educational_channels = st.selectbox("Heard via Educational Channels?", ["Yes", "No"])
+referral = st.selectbox("Heard via Referral?", ["Yes", "No"])
+
+
+lead_data = {
+    "age": age,
+    "current_occupation": current_occupation,
+    "first_interaction": first_interaction,
+    "profile_completed": profile_completed,
+    "website_visits": website_visits,
+    "time_spent_on_website": time_spent_on_website,
+    "page_views_per_visit": page_views_per_visit,
+    "last_activity": last_activity,
+    "print_media_type1": print_media_type1,
+    "print_media_type2": print_media_type2,
+    "digital_media": digital_media,
+    "educational_channels": educational_channels,
+    "referral": referral
+}
+
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://HarishMaths-Learn-Model-API.hf.space/v1/predict", json=product_data)  # Replace <user_name> and <space_name>
+    if response.status_code == 200:
+        result = response.json()
+        predicted_value = result["Lead"]
+        st.write("Predicted :",predicted_value)
+    else:
+        st.error("Error in API request")

requirements.txt

@@ -1,3 +1,2 @@
-altair
-pandas
-streamlit
+requests==2.32.3
+streamlit==1.45.0