Update app.py

app.py

@@ -1,29 +1,17 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import google.generativeai as genai
+import os
+from huggingface_hub import login
 
-# Available models for code explanation
-MODELS = {
-    "CodeT5-small-sum": "Salesforce/codet5-small",
-    "CodeT5-base-sum": "Salesforce/codet5-base-multi-sum"
-}
+# Use your Hugging Face variable for the Gemini API key
+GEMINI_API_KEY = os.getenv("GOOGLEAPI", "PASTE_YOUR_GEMINI_API_KEY_HERE")
+genai.configure(api_key=GEMINI_API_KEY)
 
-def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    # Use T5 for Seq2Seq, fallback to text2text-generation for others
-    if "codet5" in model_name.lower():
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-        return pipeline("summarization", model=model, tokenizer=tokenizer)
-    else:
-        # fallback for unsupported models
-        return pipeline("text2text-generation", model=model_name, tokenizer=tokenizer)
-
-def explain_code(files, model_choice):
-    model_id = MODELS[model_choice]
-    explainer = load_model(model_id)
+def explain_code(files, _):
+    model = genai.GenerativeModel("gemini-pro")
     explanations = []
     for file in files:
         filename = file.name if hasattr(file, 'name') else 'Uploaded File'
-        # For Gradio 'binary' type, file is bytes, not a file-like object
         if isinstance(file, bytes):
             code = file.decode('utf-8')
         elif hasattr(file, 'read'):
@@ -34,27 +22,21 @@ def explain_code(files, model_choice):
         line_explanations = []
         for idx, line in enumerate(lines):
             if not line.strip():
-                continue  # Skip empty lines
-            input_length = len(line.split())
-            max_length = max(1, input_length // 2)
-            gen_kwargs = dict(max_length=max_length, do_sample=False)
-            if "codet5" in model_id.lower():
-                result = explainer(line, **gen_kwargs)[0].get('summary_text', '')
-            else:
-                prompt = f"Explain this line of code:\n{line}"
-                result = explainer(prompt, **gen_kwargs)[0].get('generated_text', '')
+                continue
+            prompt = f"Explain this line of code:\n{line}"
+            response = model.generate_content(prompt)
+            result = response.text if hasattr(response, 'text') else str(response)
             line_explanations.append(f"Line {idx+1}: {line}\nExplanation: {result.strip()}\n")
         explanations.append(f"## {filename}\n" + "\n".join(line_explanations))
     return "\n\n".join(explanations)
 
 def main():
     with gr.Blocks() as demo:
-        gr.Markdown("# CodeExplainerBot (Hugging Face UI)")
-        model_choice = gr.Dropdown(list(MODELS.keys()), label="Select a model", value="CodeT5-small-sum")
+        gr.Markdown("# CodeExplainerBot (Gemini API)")
         code_input = gr.File(label="Upload code files", file_count="multiple", type="binary")
         output = gr.Textbox(label="Line-by-line Explanation", lines=20)
         explain_btn = gr.Button("Explain Code")
-        explain_btn.click(fn=explain_code, inputs=[code_input, model_choice], outputs=output)
+        explain_btn.click(fn=explain_code, inputs=[code_input, None], outputs=output)
     demo.launch()
 
 if __name__ == "__main__":