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.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,6 @@
 ---
-title: AI Course Generator
-emoji: 🌖
-colorFrom: indigo
-colorTo: indigo
+title: AI_Course_Generator
+app_file: gradio_ui.py
 sdk: gradio
 sdk_version: 5.14.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

gradio_ui.py

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+import gradio as gr
+import requests
+import time  # For simulating a loading effect
+
+# API Endpoints
+LOGIN_URL = "http://127.0.0.1:8000/auth/v1/login"
+COURSE_API_URL = "http://127.0.0.1:8000/ai_course/v1/generate-course/"
+
+# Hardcoded user credentials (Ideally, this should be entered dynamically)
+USERNAME = "aankitroy1990@gmail.com"
+PASSWORD = "xyzpassword"
+
+# Function to authenticate and get access token
+def get_access_token():
+    try:
+        response = requests.post(
+            LOGIN_URL,
+            data={
+                "grant_type": "password",
+                "username": USERNAME,
+                "password": PASSWORD,
+                "scope": "",
+                "client_id": "string",
+                "client_secret": "string",
+            },
+            headers={"Content-Type": "application/x-www-form-urlencoded"},
+        )
+        response_data = response.json()
+
+        if "access_token" in response_data:
+            return response_data["access_token"]
+        else:
+            return None
+    except Exception as e:
+        return print(f"Failed to get access token: {str(e)}")
+
+# Function to fetch course details with loading effect
+def get_course(course_name, goal, course_type, proficiency, difficulty):
+    yield "**Generating course... Please wait.** ⏳"  # Show loading message
+
+    access_token = get_access_token()
+    if not access_token:
+        yield "**❌ Authentication failed. Please check credentials.**"
+        return
+
+    headers = {"Authorization": f"Bearer {access_token}"}
+    payload = {
+        "course_name": course_name,
+        "goal": goal,
+        "course_type": course_type,
+        "proficiency": proficiency,
+        "difficulty": difficulty,
+    }
+
+    try:
+        response = requests.post(COURSE_API_URL, json=payload, headers=headers)
+        response_data = response.json()
+
+        if "course_content" in response_data:
+            yield response_data["course_content"]  # Final response
+        else:
+            yield "**⚠️ Error: Unexpected response from server.**"
+    except Exception as e:
+        yield f"**❌ Failed to parse response: {str(e)}**"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=get_course,
+    inputs=[
+        gr.Textbox(label="Course Name"),
+        gr.Textbox(label="Goal/Target"),
+        gr.Radio(["Micro", "Full"], label="Course Type"),
+        gr.Radio(["Beginner", "Intermediate", "Advanced"], label="Proficiency"),
+        gr.Radio(["Easy", "Medium", "Hard"], label="Difficulty"),
+    ],
+    outputs=gr.Markdown(label="Generated Course"),
+    title="AI Course Generator",
+    description="Create structured courses with AI.",
+)
+
+# Launch with sharing enabled
+iface.launch(share=True)