Update app.py

app.py

@@ -1,4 +1,119 @@
 from huggingfaceinferenceclient import HuggingFaceInferenceClient
 from outpaintprocessor import DynamicImageOutpainter
 from aivideopipeline import AIImageVideoPipeline
-from mmig import MultiModelImageGenerator
+from mmig import MultiModelImageGenerator
+
+
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+from huggingface_hub import InferenceClient
+from IPython.display import Audio, display
+import gradio as gr
+
+# Whisper for Speech-to-Text
+WHISPER_API_URL = "https://api-inference.huggingface.co/models/distil-whisper/distil-large-v2"
+WHISPER_HEADERS = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+
+def speech_to_text(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(WHISPER_API_URL, headers=WHISPER_HEADERS, data=data)
+    if response.status_code == 200:
+        return response.json().get("text", "Could not recognize speech")
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
+        return None
+
+# Chatbot Logic with Hugging Face InferenceClient
+client = InferenceClient(api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
+
+def chatbot_logic(input_text):
+    messages = [{"role": "user", "content": input_text}]
+    try:
+        completion = client.chat.completions.create(
+            model="mistralai/Mistral-Nemo-Instruct-2407", 
+            messages=messages, 
+            max_tokens=500
+        )
+        return completion.choices[0].message["content"]
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+# Bark for Text-to-Speech
+BARK_API_URL = "https://api-inference.huggingface.co/models/suno/bark"
+BARK_HEADERS = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+
+def text_to_speech(text):
+    payload = {"inputs": text}
+    response = requests.post(BARK_API_URL, headers=BARK_HEADERS, json=payload)
+    if response.status_code == 200:
+        return response.content
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
+        return None
+
+# Flux for Image Generation
+FLUX_API_URL = "https://api-inference.huggingface.co/models/enhanceaiteam/Flux-uncensored"
+FLUX_HEADERS = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+
+def generate_image(prompt):
+    data = {"inputs": prompt}
+    response = requests.post(FLUX_API_URL, headers=FLUX_HEADERS, json=data)
+    if response.status_code == 200:
+        image_bytes = BytesIO(response.content)
+        return Image.open(image_bytes)
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
+        return None
+
+# Gradio Interface for Chatbot and Image Generator
+def create_ui():
+    def process_chat(audio_file):
+        # Step 1: Speech to Text
+        recognized_text = speech_to_text(audio_file)
+        if not recognized_text:
+            return "Could not recognize speech", None, None
+        
+        # Step 2: Chatbot Logic
+        response_text = chatbot_logic(recognized_text)
+        if not response_text:
+            return f"Error generating response for: {recognized_text}", None, None
+        
+        # Step 3: Text to Speech
+        audio_output = text_to_speech(response_text)
+        if not audio_output:
+            return f"Error synthesizing response: {response_text}", None, None
+        
+        # Step 4: Image Generation
+        generated_image = generate_image(response_text)
+        
+        return response_text, Audio(audio_output, autoplay=True), generated_image
+
+    with gr.Blocks(title="Voice-to-Voice Chatbot with Image Generation") as ui:
+        gr.Markdown("## Voice-to-Voice Chatbot with Image Generation\nUpload an audio file to interact with the chatbot.")
+        
+        audio_input = gr.Audio(source="upload", type="filepath", label="Input Audio File")
+        submit_button = gr.Button("Process")
+        
+        with gr.Row():
+            chatbot_response = gr.Textbox(label="Chatbot Response", lines=2)
+        
+        with gr.Row():
+            audio_output = gr.Audio(label="Generated Audio Response")
+            image_output = gr.Image(label="Generated Image")
+        
+        submit_button.click(
+            fn=process_chat,
+            inputs=audio_input,
+            outputs=[chatbot_response, audio_output, image_output],
+            show_progress=True
+        )
+    
+    return ui
+
+# Run the Gradio Interface
+if __name__ == "__main__":
+    create_ui().launch(debug=True)