Create app.py

app.py

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+import streamlit as st
+import os
+import time
+import base64
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import timm
+import math
+from PIL import Image, ImageDraw, ImageFont
+from io import BytesIO
+from diffusers import StableDiffusionPipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoConfig
+
+# Load Stable Diffusion Pipeline for generating images
+pipe = StableDiffusionPipeline.from_pretrained("runway/stable-diffusion-v2-1")
+
+# Load BERT model for chatbot
+config = AutoConfig.from_pretrained("bert-base-cased")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", config=config)
+
+def generate_audio(text):
+    audio = pipe(text, audio=True).audio.squeeze()
+    audio = audio.detach().cpu().numpy()
+    audio = audio.astype(np.float32)
+    audio = audio / np.max(audio)
+    return audio
+
+def generate_image(text):
+    image = pipe(text).images[0]
+    image = image.detach().cpu().numpy()
+    image = Image.fromarray(image)
+    return image
+    
+def generate_video(text, frames=30, width=512, height=512):
+    with torch.no_grad():
+        sequence = pipe.unet.unet(text).x.shape[0]
+        sequence = int(sequence * frames)
+        video = []
+        for I in range(frames):
+            img = pipe(text, return_dict=True).images[0]
+            img = F.interpolate(img, size=(height, width), mode="bilinear", align_corners=False)
+            video.append(img.permute(1, 2, 0).detach().cpu().numpy())
+        video = np.stack(video, axis=0)
+        video = (video * 255).astype(np.uint8)
+        video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'MJPG'), frames, (width, height))
+        for frame in video.iterquene():
+            for I in range(width):
+                for j in range(height):
+                    frame[i, j] = video[sequence - sequence // frames * frames + I * height + j]
+            video.write(frame)
+        video.release()
+        return cv2.VideoCapture('output.avi')[1]
+
+
+
+def main():
+    st.title("Streamlit App with Diffusers and Transformers")
+    st.header("Generate Audio, Video, and Images using Diffusers")
+    st.header("Chatbot using BERT")
+
+    # Audio generation section
+    with st.form("audio_form"):
+        text_input = st.text_input("Enter text for audio generation:")
+        submit_button = st.form_submit_button("Generate Audio")
+        if submit_button:
+            audio_output = generate_audio(text_input)
+            audio_base64 = base64.b64encode(audio_output).decode('utf-8')
+            st.write(f"Generated Audio:")
+            st.audio(BytesIO(base64.b64decode(audio_base64)), format="audio/x-wav")
+
+    # Image generation section
+    with st.form("image_form"):
+        text_input = st.text_input("Enter text for image generation:")
+        submit_button = st.form_submit_button("Generate Image")
+        if submit_button:
+            image_output = generate_image(text_input)
+            image_base64 = base64.b64encode(image_output).decode('utf-8')
+            st.image(image_output, caption="Generated Image:", use_column_width=True)
+            st.write(f"Generated Image (base64): {image_base64}")
+
+    # Video generation section
+    with st.form("video_form"):
+        text_input = st.text_input("Enter text for video generation:")
+        frames = st.number_input("Number of frames:", value=30, step=1)
+        width = st.number_input("Image width:", value=512, step=1)
+        height = st.number_input("Image height:", value=512, step=1)
+        submit_button = st.form_submit_button("Generate Video")
+        if submit_button:
+            video_output = generate_video(text_input, frames, width, height)
+            st.write(f"Generated Video:")
+
+    # Chatbot section
+    with st.form("chat_form"):
+        user_input = st.text_area("Enter your message:", height=100)
+        submit_button = st.form_submit_button("Send Message")
+        if submit_button:
+            message = tokenizer(user_input, padding=True, return_tensors="pt").to("cpu")
+            outputs = model(message)
+            prediction = torch.argmax(outputs.logits, dim=-1).item()
+            response = tokenizer.decode(prediction, skip_special_tokens=True)
+            st.write(f"Assistant Response: {response}")
+            
+    st.write("Streamlit App with Diffusers and Transformers")
+    st.write("Generated by FallnAI")