Update app.py

app.py

@@ -1,64 +1,75 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import openai
+import pandas as pd
+import os
 
+openai.api_key = os.getenv("OPENAI_API_KEY")  # Add this in the HF Secrets tab
+
+# 🔍 Prompt Generator
+def generate_prompt(question, solution, instructions):
+    return f"""
+{instructions}
+
+---
+
+Question:
+{question}
+
+Student's Solution:
+{solution}
+
+Evaluate the student's solution and provide feedback.
+
+End your response with:
+Is the solution correct? [Yes/No]
 """
-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")
 
+# 🧠 Evaluation Function
+def evaluate_csv(file, instructions):
+    df = pd.read_csv(file.name)
+    feedback_list = []
+    status_list = []
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    for idx, row in df.iterrows():
+        q = row['question']
+        s = row['solution']
+        prompt = generate_prompt(q, s, instructions)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-4",  # or gpt-3.5-turbo
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.2
+            )
+            output = response.choices[0].message.content
+            feedback_list.append(output)
 
-    messages.append({"role": "user", "content": message})
+            # Extract Yes/No from output
+            status = "Yes" if "Yes" in output.splitlines()[-1] else "No"
+            status_list.append(status)
 
-    response = ""
+        except Exception as e:
+            feedback_list.append(f"Error: {e}")
+            status_list.append("Error")
 
-    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
+    df['AI_Feedback'] = feedback_list
+    df['Is_Correct'] = status_list
 
-        response += token
-        yield response
+    output_path = "evaluated_output.csv"
+    df.to_csv(output_path, index=False)
+    return output_path
 
+# 🎛️ Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📊 Solution Evaluator (CSV)")
+    gr.Markdown("Upload your CSV with `question` and `solution` columns. Provide evaluation instructions.")
+    with gr.Row():
+        csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
+        instructions_input = gr.Textbox(lines=6, label="Evaluation Instructions")
+    output_file = gr.File(label="Download Evaluated CSV")
 
-"""
-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()
+    run_btn = gr.Button("Evaluate Solutions")
+
+    run_btn.click(fn=evaluate_csv, inputs=[csv_input, instructions_input], outputs=output_file)
+
+demo.launch()