Update app.py

app.py

@@ -1,64 +1,129 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import numpy as np
+import librosa
+import cv2
+import moviepy.editor as mp
+import speech_recognition as sr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from collections import Counter
+
+# Load necessary models and files
+text_model = load_model('model_for_text_emotion_updated(1).keras')  # Load your text emotion model
+with open('tokenizer.json') as json_file:
+    tokenizer = tokenizer_from_json(json.load(json_file))  # Tokenizer for text emotion
+audio_model = load_model('my_model.h5')  # Load audio emotion model
+image_model = load_model('model_emotion.h5')  # Load image emotion model
+
+# Load LLM model from Hugging Face
+llama_model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")  # Example: small OPT model
+llama_tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
+
+# Emotion mapping (from your model output)
+emotion_mapping = {0: "anger", 1: "disgust", 2: "fear", 3: "joy", 4: "neutral", 5: "sadness", 6: "surprise"}
+
+# Preprocess text for emotion prediction
+def preprocess_text(text):
+    tokens = [word for word in text.lower().split() if word.isalnum()]
+    return ' '.join(tokens)
+
+# Predict emotion from text
+def predict_text_emotion(text):
+    preprocessed_text = preprocess_text(text)
+    seq = tokenizer.texts_to_sequences([preprocessed_text])
+    padded_seq = pad_sequences(seq, maxlen=35)
+    prediction = text_model.predict(padded_seq)
+    emotion_index = np.argmax(prediction)
+    return emotion_mapping[emotion_index]
+
+# Extract audio features and predict emotion
+def extract_audio_features(audio_data, sample_rate):
+    mfcc = np.mean(librosa.feature.mfcc(y=audio_data, sr=sample_rate).T, axis=0)
+    return np.expand_dims(mfcc, axis=0)
+
+def predict_audio_emotion(audio_data, sample_rate):
+    features = extract_audio_features(audio_data, sample_rate)
+    prediction = audio_model.predict(features)
+    emotion_index = np.argmax(prediction)
+    return emotion_mapping[emotion_index]
+
+# Process video and predict emotions from frames
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frame_rate = cap.get(cv2.CAP_PROP_FPS)
+    predictions = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if int(cap.get(cv2.CAP_PROP_POS_FRAMES)) % int(frame_rate) == 0:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            frame = cv2.resize(frame, (48, 48))
+            frame = img_to_array(frame) / 255.0
+            frame = np.expand_dims(frame, axis=0)
+            prediction = image_model.predict(frame)
+            predictions.append(np.argmax(prediction))
+
+    cap.release()
+    most_common_emotion = Counter(predictions).most_common(1)[0][0]
+    return emotion_mapping[most_common_emotion]
+
+# Extract audio from video and process
+def extract_audio_from_video(video_path):
+    video = mp.VideoFileClip(video_path)
+    audio = video.audio
+    audio_file = 'audio.wav'
+    audio.write_audiofile(audio_file)
+    return audio_file
+
+def transcribe_audio(audio_file):
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(audio_file) as source:
+        audio_record = recognizer.record(source)
+        return recognizer.recognize_google(audio_record)
+
+# Integrating with LLM to adjust responses based on detected emotion
+def interact_with_llm(emotion, user_input):
+    prompt = f"The user is feeling {emotion}. Respond to their question in an empathetic and appropriate manner: {user_input}"
+    
+    inputs = llama_tokenizer(prompt, return_tensors="pt")
+    outputs = llama_model.generate(**inputs, max_length=200)
+    response = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
+
+# Main function to process video and predict emotions
+def transcribe_and_predict_video(video_path):
+    # Extract audio from video and predict text-based emotion
+    audio_file = extract_audio_from_video(video_path)
+    text = transcribe_audio(audio_file)
+    text_emotion = predict_text_emotion(text)
+
+    # Predict emotion from video frames (image-based)
+    image_emotion = process_video(video_path)
+
+    # Predict emotion from audio (sound-based)
+    sample_rate, audio_data = librosa.load(audio_file, sr=None)
+    audio_emotion = predict_audio_emotion(audio_data, sample_rate)
+
+    # Combine the detected emotions for final output (you could average them or choose the most common)
+    final_emotion = image_emotion  # Or decide based on some logic (e.g., majority vote)
+
+    # Get response from LLM
+    llm_response = interact_with_llm(final_emotion, text)
+
+    return f"Emotion Detected: {final_emotion}\nLLM Response: {llm_response}"
+
+# Create Gradio interface
+iface = gr.Interface(fn=transcribe_and_predict_video, 
+                     inputs=gr.Video(), 
+                     outputs="text", 
+                     title="Emotion-Responsive LLM for Video",
+                     description="Upload a video to get emotion predictions and LLM responses based on detected emotions.")
+
+iface.launch()