Update app.py

app.py

@@ -9,6 +9,8 @@ from torchvision.models import resnet50
 
 # Initialize inference client for chat
 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)
@@ -21,15 +23,14 @@ transform = transforms.Compose([
 ])
 
 def search_wikipedia(query):
-    try:
-        summary = wikipedia.summary(query)
-        return summary
-    except wikipedia.exceptions.DisambiguationError as e:
-        return f"Disambiguation error: {e}"
-    except wikipedia.exceptions.PageError:
+    page = wiki_wiki.page(query)
+    if page.exists():
+        return page.summary
+    else:
         return "No information found on that topic."
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
+    # Search Wikipedia for information
     search_response = search_wikipedia(message)
     
     # Prepare the chat messages
@@ -62,53 +63,57 @@ def classify_image(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("## 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.Tab("Chatbot with Wikipedia Search"):
+        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.Chatbot(label="Chat History")
+                user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+                submit_btn = gr.Button("Send")
 
-        with gr.Column():
-            chat_output = gr.Chatbot(label="Chat History")
-            user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
-            submit_btn = gr.Button("Send")
+        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
 
-            # 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")
+        submit_btn.click(on_submit, inputs=[user_input, chat_output], outputs=[chat_output, gr.Textbox(label="Wikipedia Summary")])
+
+    with gr.Tab("Image Classification"):
+        image_input = gr.Image(type="pil", label="Upload an Image")
+        classify_btn = gr.Button("Classify Image")
+        classification_output = gr.Textbox(label="Classification Result")
+        
+        classify_btn.click(classify_image, inputs=image_input, outputs=classification_output)
+
+    with gr.Tab("Video Generation"):
+        video_input = gr.Video(label="Upload a Video")
+        generate_video_btn = gr.Button("Generate Video")
+        video_output = gr.Video(label="Generated Video")
+        
+        # Placeholder for video generation logic (implement as needed)
+        def generate_video(video):
+            return video  # Just returns the input video for now
+
+        generate_video_btn.click(generate_video, inputs=video_input, outputs=video_output)
 
-    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
+    with gr.Tab("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")
+        
+        # Placeholder for video classification logic (implement as needed)
+        def classify_video(video):
+            return "Video classification logic not implemented."  # Placeholder
 
-    # 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)
+        classify_video_btn.click(classify_video, inputs=video_class_input, outputs=video_classification_output)
 
 if __name__ == "__main__":
     demo.launch()