Create app.py

app.py

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+import gradio as gr
+import transformers  # Replace with your specific library if needed
+
+# Load your custom models (example)
+model_name = "microsoft/Phi-3-mini-4k-instruct"  # Replace with your model name
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+
+def chatbot_response(user_input):
+    inputs = tokenizer.encode(user_input, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+def upload_readme(file):
+    if file is not None:
+        content = file.read().decode("utf-8")
+        return content
+    return "No file uploaded"
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("# Chatbot Interface")
+            gr.Markdown("Upload your README file and interact with the chatbot.")
+            
+            # File upload
+            readme_file = gr.File(label="Upload README file", type="file", file_count="single", file_types=[".md"])
+            readme_content = gr.Textbox(label="README Content", lines=10, placeholder="README content will appear here...")
+            
+            # Button to display README content
+            readme_file.change(upload_readme, inputs=readme_file, outputs=readme_content)
+            
+            # Chatbot input and output
+            user_input = gr.Textbox(label="Your message", placeholder="Type your message here...")
+            output = gr.Textbox(label="Chatbot response", placeholder="Chatbot response will appear here...", lines=5)
+            
+            # Button to get chatbot response
+            user_input.submit(chatbot_response, inputs=user_input, outputs=output)
+
+demo.launch()