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UPSC Bot for Learning Corner

apikey.env

@@ -0,0 +1 @@
+OPENAI_API_KEY=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

app.py

@@ -1,63 +1,45 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import openai
+import gradio as gr
+from dotenv import load_dotenv
+import os
+
+# Load API key from .env file
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Function to generate a response from OpenAI
+def generate_response(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",  # Use the latest model
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant for UPSC exam preparation."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=150,
+        n=1,
+        stop=None,
+        temperature=0.7
+    )
+    return response.choices[0].message['content'].strip()
+
+# Gradio interface
+def chatbot_interface(input_text):
+    return generate_response(input_text)
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=chatbot_interface,
+    inputs=gr.Textbox(lines=7, placeholder="Ask your UPSC question here..."),
+    outputs="text",
+    title="UPSC Exam Preparation Bot",
+    description="A bot to help you prepare for the UPSC Examination.",
+    examples=[
+        ["What are the main features of the Indian Constitution?"],
+        ["Can you give me tips on how to prepare for the UPSC prelims?"],
+        ["What are the important current affairs topics for this year?"]
+    ]
+)
+
+# Launch the interface with public sharing enabled
+if __name__ == "__main__":
+    iface.launch(share=True)

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.22.2
+openai
+gradio
+python-dotenv