Update app.py

app.py

@@ -1,12 +1,27 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import wikipediaapi
+from PIL import Image
+import requests
+import torch
+from torchvision import transforms
+from torchvision.models import resnet50
 
 # Initialize inference client for chat
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+chat_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 # Initialize Wikipedia API
 wiki_wiki = wikipediaapi.Wikipedia('en')
 
+# Load pre-trained image classification model
+model = resnet50(pretrained=True)
+model.eval()
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
 def search_wikipedia(query):
     page = wiki_wiki.page(query)
     if page.exists():
@@ -30,7 +45,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     
     # Generate response from chat model
     response = ""
-    for message in client.chat_completion(
+    for message in chat_client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
@@ -41,28 +56,60 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         response += token
         yield response, search_response  # Return both responses
 
+def classify_image(image):
+    image = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(image)
+    _, predicted = torch.max(output, 1)
+    return f"Predicted class index: {predicted.item()}"
+
+# Placeholder functions for video generation and classification
+def generate_video(video):
+    return video  # Placeholder: Just returns the input video for now
+
+def classify_video(video):
+    return "Video classification logic not implemented."  # Placeholder
+
 # Gradio interface setup using Blocks
 with gr.Blocks() as demo:
-    gr.Markdown("## Chatbot with Wikipedia Search")
+    gr.Markdown("## Multi-Functional AI Interface")
+    
     with gr.Row():
         with gr.Column():
             system_message = gr.Textbox(value="You are a friendly Chatbot named Tirth.", label="System message")
             max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
             temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
             top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
-        
+
         with gr.Column():
             chat_output = gr.Chatbox(label="Chat History")
             user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
             submit_btn = gr.Button("Send")
 
-    # Function to handle button click
+            # Image Classification
+            image_input = gr.Image(type="pil", label="Upload an Image for Classification")
+            classify_btn = gr.Button("Classify Image")
+            classification_output = gr.Textbox(label="Classification Result")
+            
+            # Video Generation
+            video_input = gr.Video(label="Upload a Video for Generation")
+            generate_video_btn = gr.Button("Generate Video")
+            video_output = gr.Video(label="Generated Video")
+            
+            # Video Classification
+            video_class_input = gr.Video(label="Upload a Video for Classification")
+            classify_video_btn = gr.Button("Classify Video")
+            video_classification_output = gr.Textbox(label="Video Classification Result")
+
     def on_submit(message, history):
         response, search_response = respond(message, history, system_message.value, max_tokens.value, temperature.value, top_p.value)
         return history + [(message, response)], search_response
 
     # Connect the button to the submission function
     submit_btn.click(on_submit, inputs=[user_input, chat_output], outputs=[chat_output, gr.Textbox(label="Wikipedia Summary")])
+    classify_btn.click(classify_image, inputs=image_input, outputs=classification_output)
+    generate_video_btn.click(generate_video, inputs=video_input, outputs=video_output)
+    classify_video_btn.click(classify_video, inputs=video_class_input, outputs=video_classification_output)
 
 if __name__ == "__main__":
     demo.launch()