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Files changed (19) hide show
  1. .gitattributes +5 -0
  2. Pipfile +69 -0
  3. Pipfile.lock +1165 -0
  4. README.md +151 -11
  5. Temp.mp3 +0 -0
  6. acne.jpg +3 -0
  7. ai-doctor-voicebit-ppt.pdf +3 -0
  8. brain_of_the_doctor.py +48 -0
  9. dandruff-optimized.webp +3 -0
  10. final.mp3 +3 -0
  11. gradio_app.py +185 -0
  12. gtts_testing.mp3 +0 -0
  13. gtts_testing_autoplay.mp3 +0 -0
  14. patient_voice_test_for_patient.mp3 +0 -0
  15. presentation.pdf +3 -0
  16. requirements.txt +59 -0
  17. tempCodeRunnerFile.py +57 -0
  18. voice_of_the_doctor.py +95 -0
  19. voice_of_the_patient.py +69 -0
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README.md CHANGED
@@ -1,14 +1,154 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
- title: Firstapp
3
- emoji: 🔥
4
- colorFrom: gray
5
- colorTo: blue
6
- sdk: gradio
7
- sdk_version: 5.49.1
8
- app_file: app.py
9
- pinned: false
10
- license: mit
11
- short_description: this is my first app
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12
  ---
13
 
14
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Project Setup Guide
2
+
3
+ This guide provides step-by-step instructions to set up your project environment, including the installation of FFmpeg and PortAudio across macOS, Linux, and Windows, as well as setting up a Python virtual environment using Pipenv, pip, or conda.
4
+
5
+ ## Table of Contents
6
+
7
+ 1. [Installing FFmpeg and PortAudio](#installing-ffmpeg-and-portaudio)
8
+ - [macOS](#macos)
9
+ - [Linux](#linux)
10
+ - [Windows](#windows)
11
+ 2. [Setting Up a Python Virtual Environment](#setting-up-a-python-virtual-environment)
12
+ - [Using Pipenv](#using-pipenv)
13
+ - [Using pip and venv](#using-pip-and-venv)
14
+ - [Using Conda](#using-conda)
15
+ 3. [Running the application](#project-phases-and-python-commands)
16
+
17
+ ## Installing FFmpeg and PortAudio
18
+
19
+ ### macOS
20
+
21
+ 1. **Install Homebrew** (if not already installed):
22
+
23
+ ```bash
24
+ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
25
+ ```
26
+
27
+ 2. **Install FFmpeg and PortAudio:**
28
+
29
+ ```bash
30
+ brew install ffmpeg portaudio
31
+ ```
32
+
33
+
34
+ ### Linux
35
+ For Debian-based distributions (e.g., Ubuntu):
36
+
37
+ 1. **Update the package list**
38
+
39
+ ```
40
+ sudo apt update
41
+ ```
42
+
43
+ 2. **Install FFmpeg and PortAudio:**
44
+ ```
45
+ sudo apt install ffmpeg portaudio19-dev
46
+ ```
47
+
48
+ ### Windows
49
+
50
+ #### Download FFmpeg:
51
+ 1. Visit the official FFmpeg download page: [FFmpeg Downloads](https://ffmpeg.org/download.html)
52
+ 2. Navigate to the Windows builds section and download the latest static build.
53
+
54
+ #### Extract and Set Up FFmpeg:
55
+ 1. Extract the downloaded ZIP file to a folder (e.g., `C:\ffmpeg`).
56
+ 2. Add the `bin` directory to your system's PATH:
57
+ - Search for "Environment Variables" in the Start menu.
58
+ - Click on "Edit the system environment variables."
59
+ - In the System Properties window, click on "Environment Variables."
60
+ - Under "System variables," select the "Path" variable and click "Edit."
61
+ - Click "New" and add the path to the `bin` directory (e.g., `C:\ffmpeg\bin`).
62
+ - Click "OK" to apply the changes.
63
+
64
+ #### Install PortAudio:
65
+ 1. Download the PortAudio binaries from the official website: [PortAudio Downloads](http://www.portaudio.com/download.html)
66
+ 2. Follow the installation instructions provided on the website.
67
+
68
  ---
69
+
70
+ ## Setting Up a Python Virtual Environment
71
+
72
+ ### Using Pipenv
73
+ 1. **Install Pipenv (if not already installed):**
74
+ ```
75
+ pip install pipenv
76
+ ```
77
+
78
+ 2. **Install Dependencies with Pipenv:**
79
+
80
+ ```
81
+ pipenv install
82
+ ```
83
+
84
+ 3. **Activate the Virtual Environment:**
85
+
86
+ ```
87
+ pipenv shell
88
+ ```
89
+
90
+ ---
91
+
92
+ ### Using `pip` and `venv`
93
+ #### Create a Virtual Environment:
94
+ ```
95
+ python -m venv venv
96
+ ```
97
+
98
+ #### Activate the Virtual Environment:
99
+ **macOS/Linux:**
100
+ ```
101
+ source venv/bin/activate
102
+ ```
103
+
104
+ **Windows:**
105
+ ```
106
+ venv\Scripts\activate
107
+ ```
108
+
109
+ #### Install Dependencies:
110
+ ```
111
+ pip install -r requirements.txt
112
+ ```
113
+
114
  ---
115
 
116
+ ### Using Conda
117
+ #### Create a Conda Environment:
118
+ ```
119
+ conda create --name myenv python=3.11
120
+ ```
121
+
122
+ #### Activate the Conda Environment:
123
+ ```
124
+ conda activate myenv
125
+ ```
126
+
127
+ #### Install Dependencies:
128
+ ```
129
+ pip install -r requirements.txt
130
+ ```
131
+
132
+
133
+ # Project Phases and Python Commands
134
+
135
+ ## Phase 1: Brain of the doctor
136
+ ```
137
+ python brain_of_the_doctor.py
138
+ ```
139
+
140
+ ## Phase 2: Voice of the patient
141
+ ```
142
+ python voice_of_the_patient.py
143
+ ```
144
+
145
+ ## Phase 3: Voice of the doctor
146
+ ```
147
+ python voice_of_the_doctor.py
148
+ ```
149
+
150
+ ## Phase 4: Setup Gradio UI
151
+ ```
152
+ python gradio_app.py
153
+ ```
154
+
Temp.mp3 ADDED
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acne.jpg ADDED

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ai-doctor-voicebit-ppt.pdf ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:455416172a4edb33075de35f4c2d3064659f36f6d55c6de0badc5e26e1777c1a
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1
+ from dotenv import load_dotenv
2
+ load_dotenv()
3
+
4
+ #Step1: Setup GROQ API key
5
+ import os
6
+ GROQ_API_KEY = os.getenv("GROQ_API_KEY")
7
+
8
+ #Step2: Convert image to required format
9
+ import base64
10
+
11
+ def encode_image(image_path):
12
+ with open(image_path, "rb") as image_file:
13
+ return base64.b64encode(image_file.read()).decode('utf-8')
14
+
15
+ #Step3: Setup Multimodal LLM
16
+ from groq import Groq
17
+
18
+ query="Is there something wrong with my face?"
19
+ model="meta-llama/llama-4-scout-17b-16e-instruct"
20
+
21
+ def analyze_image_with_query(query, model, encoded_image):
22
+ client = Groq(api_key=GROQ_API_KEY) # FIXED HERE
23
+ messages = [
24
+ {
25
+ "role": "user",
26
+ "content": [
27
+ {"type": "text", "text": query},
28
+ {"type": "image_url",
29
+ "image_url": {
30
+ "url": f"data:image/jpeg;base64,{encoded_image}",
31
+ },
32
+ },
33
+ ],
34
+ }
35
+ ]
36
+
37
+ chat_completion = client.chat.completions.create(
38
+ messages=messages,
39
+ model=model
40
+ )
41
+
42
+ return chat_completion.choices[0].message.content
43
+
44
+ # MAIN EXECUTION
45
+ if __name__ == "__main__":
46
+ encoded_img = encode_image("acne.jpg") # make sure acne.jpg exists same folder
47
+ result = analyze_image_with_query(query, model, encoded_img)
48
+ print(result)
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+ size 327552
gradio_app.py ADDED
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1
+ # gradio_app.py
2
+ # if you dont use pipenv uncomment the following:
3
+ # from dotenv import load_dotenv
4
+ # load_dotenv()
5
+
6
+ import os
7
+ import json
8
+ import gradio as gr
9
+ from pathlib import Path
10
+ from brain_of_the_doctor import encode_image, analyze_image_with_query
11
+ from voice_of_the_patient import transcribe_with_groq
12
+ from voice_of_the_doctor import text_to_speech_with_gtts
13
+
14
+ # --- Configuration ---
15
+ MODEL_NAME = "meta-llama/llama-4-scout-17b-16e-instruct" # your choice A
16
+ GROQ_KEY = os.environ.get("GROQ_API_KEY")
17
+
18
+ # System prompt tuned for Format 2 + JSON output (analysis short, treatment longer)
19
+ SYSTEM_PROMPT_TEMPLATE = """
20
+ You are a professional medical doctor speaking directly to a patient.
21
+ You will be given an image to analyze and an optional transcription of the patient's spoken context.
22
+ Produce a JSON object (and nothing else) with exactly two keys: "analysis" and "treatment".
23
+
24
+ Rules:
25
+ - "analysis": one or two concise sentences describing what appears medically wrong in the image. Use a natural doctor voice, start with "With what I see, ..." and keep it short.
26
+ - "treatment": 3 to 4 sentences giving practical remedies, next steps, and when to seek professional care. Use a natural doctor voice, clear steps, no bullet points or numbered lists.
27
+ - Do not include any extra keys, commentary, or markdown. Respond ONLY with valid JSON.
28
+
29
+ If the image is missing or unclear, set "analysis" to "Image not provided or unclear" and give a general short treatment in "treatment".
30
+ """
31
+
32
+ # --- Helpers ---
33
+ def safe_parse_json(model_text: str):
34
+ """
35
+ Try to parse model_text as JSON. If it fails, do a best-effort extraction:
36
+ look for the first {...} block and parse that. If still fails, return
37
+ fallback values.
38
+ """
39
+ # direct parse
40
+ try:
41
+ return json.loads(model_text)
42
+ except Exception:
43
+ pass
44
+
45
+ # try to find first JSON object in text
46
+ start = model_text.find("{")
47
+ end = model_text.rfind("}")
48
+ if start != -1 and end != -1 and end > start:
49
+ try:
50
+ candidate = model_text[start:end+1]
51
+ return json.loads(candidate)
52
+ except Exception:
53
+ pass
54
+
55
+ # fallback: return the whole text in treatment and a generic analysis
56
+ return {
57
+ "analysis": "Could not parse structured analysis from the model output.",
58
+ "treatment": model_text.strip()[:2000] # keep the raw text as treatment fallback
59
+ }
60
+
61
+ # --- Core processing function ---
62
+ def process_inputs(audio_filepath, image_filepath):
63
+ # 1) STT (optional)
64
+ stt_text = ""
65
+ try:
66
+ if audio_filepath:
67
+ stt_text = transcribe_with_groq(GROQ_API_KEY=GROQ_KEY, audio_filepath=audio_filepath, stt_model="whisper-large-v3") or ""
68
+ else:
69
+ stt_text = ""
70
+ except Exception as e:
71
+ stt_text = f"[STT error: {str(e)}]"
72
+
73
+ # 2) Build prompt for LLM
74
+ assembled_prompt = SYSTEM_PROMPT_TEMPLATE + "\n\n"
75
+ if stt_text:
76
+ assembled_prompt += "Patient speech (transcription): " + stt_text + "\n\n"
77
+
78
+ if image_filepath:
79
+ # encode image and pass it to your analyze function which should send image + prompt to the model
80
+ try:
81
+ encoded = encode_image(image_filepath)
82
+ model_raw_output = analyze_image_with_query(query=assembled_prompt, encoded_image=encoded, model=MODEL_NAME)
83
+ except Exception as e:
84
+ model_raw_output = json.dumps({
85
+ "analysis": "Image processing error",
86
+ "treatment": f"Failed to analyze image due to error: {str(e)}"
87
+ })
88
+ else:
89
+ # If no image, instruct the model accordingly
90
+ assembled_prompt += "No image provided."
91
+ try:
92
+ model_raw_output = analyze_image_with_query(query=assembled_prompt, encoded_image=None, model=MODEL_NAME)
93
+ except Exception as e:
94
+ model_raw_output = json.dumps({
95
+ "analysis": "Image not provided or unclear",
96
+ "treatment": f"No image was provided. If you can, please upload a clear photo. Error detail: {str(e)}"
97
+ })
98
+
99
+ # 3) Parse model output (expect JSON)
100
+ parsed = safe_parse_json(model_raw_output)
101
+
102
+ # ensure keys exist
103
+ analysis = parsed.get("analysis", "Analysis not available.")
104
+ treatment = parsed.get("treatment", "Treatment not available.")
105
+
106
+ # 4) Generate TTS audio for the doctor's combined response (you can choose what text to read out)
107
+ # we'll speak a short combined voice output: analysis + treatment summary
108
+ tts_text = f"{analysis} {treatment}"
109
+ tts_path = "final.mp3"
110
+ try:
111
+ # ensure previous file removed to avoid conflicts
112
+ if Path(tts_path).exists():
113
+ Path(tts_path).unlink()
114
+ text_to_speech_with_gtts(input_text=tts_text, output_filepath=tts_path)
115
+ except Exception as e:
116
+ # if TTS fails, keep audio empty and append error to treatment
117
+ tts_path = None
118
+ treatment = f"{treatment}\n\n[TTS generation failed: {str(e)}]"
119
+
120
+ # 5) Return values in the order: Speech-to-text, analysis, treatment, audio filepath (or None)
121
+ return stt_text, analysis, treatment, tts_path
122
+
123
+ # --- Build Enhanced UI with gradio Blocks ---
124
+ css = """
125
+ /* small medical-style theme */
126
+ body { font-family: Inter, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue'; }
127
+ .gradio-container { --primary-hue: 207; } /* blue tone */
128
+ .header { text-align:center; margin-bottom: 12px; }
129
+ .app-card { background: #ffffff; border-radius: 12px; padding: 18px; box-shadow: 0 6px 18px rgba(15,23,42,0.06); }
130
+ .label-quiet { color: #6b7280; font-size: 13px; }
131
+ .big-title { font-size: 26px; font-weight:700; color:#0b3b66; }
132
+ .small-note { color:#475569; font-size:13px; }
133
+ """
134
+
135
+ with gr.Blocks(css=css, title="AI response with Vision and Voice (Medical)") as demo:
136
+ with gr.Column(elem_id="top", scale=1):
137
+ gr.Markdown("<div class='header'><div class='big-title'>AI response with Vision and Voice</div>"
138
+ "<div class='small-note'>Record or upload voice, upload a medical image, then press Submit</div></div>")
139
+
140
+ with gr.Row():
141
+ # Left column: inputs
142
+ with gr.Column(scale=6):
143
+ with gr.Group(elem_id="left_group", visible=True):
144
+ gr.Markdown("#### Patient Input", elem_classes="label-quiet")
145
+ audio_in = gr.Audio(sources=["microphone"], type="filepath", label="Record Voice (click to start/stop)")
146
+ image_in = gr.Image(type="filepath", label="Upload Medical Image (optional)")
147
+ with gr.Row():
148
+ submit_btn = gr.Button("Submit", variant="primary")
149
+ clear_btn = gr.Button("Clear", variant="secondary")
150
+
151
+ # Right column: outputs
152
+ with gr.Column(scale=6):
153
+ with gr.Group(elem_id="right_card", visible=True):
154
+ gr.Markdown("#### Results", elem_classes="label-quiet")
155
+ stt_out = gr.Textbox(label="Speech to Text", interactive=False)
156
+ analysis_out = gr.Textbox(label="Medical Analysis (1-2 sentences)", interactive=False)
157
+ treatment_out = gr.Textbox(label="Treatment / Next Steps (3-4 sentences)", interactive=False)
158
+ audio_out = gr.Audio(label="response output(playable)", interactive=False)
159
+
160
+ # small note and flag
161
+ flag_btn = gr.Button("Flag", visible=True)
162
+ status = gr.Label(value="", visible=False)
163
+
164
+ # Action wiring
165
+ def on_submit(audio, image):
166
+ # UI feedback while processing
167
+ status_msg = "Processing... this may take a few seconds"
168
+ return gr.update(value=status_msg), *process_inputs(audio, image)
169
+
170
+ # When user clicks Submit -> call process_inputs and update outputs
171
+ submit_btn.click(fn=process_inputs, inputs=[audio_in, image_in], outputs=[stt_out, analysis_out, treatment_out, audio_out])
172
+
173
+ # Clear button resets inputs & outputs
174
+ def clear_all():
175
+ return None, None, None, None, None
176
+ clear_btn.click(lambda: (None, None, "", "", "", None), inputs=None, outputs=[audio_in, image_in, stt_out, analysis_out, treatment_out, audio_out])
177
+
178
+ # Optional: flag button to capture attention (no-op here, replace with logging)
179
+ def flag_action(analysis_text, treatment_text):
180
+ # you could log flagged cases to a database here
181
+ return gr.update(value="Flagged — thanks. We'll review this case."),
182
+ flag_btn.click(flag_action, inputs=[analysis_out, treatment_out], outputs=[status])
183
+
184
+ # Launch
185
+ demo.launch(debug=True, share=False)
gtts_testing.mp3 ADDED
Binary file (37.6 kB). View file
 
gtts_testing_autoplay.mp3 ADDED
Binary file (36.3 kB). View file
 
patient_voice_test_for_patient.mp3 ADDED
Binary file (42.7 kB). View file
 
presentation.pdf ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f654a8e0d60799bc74e702264c3ea436e43f801124db0ab61d31926e2175bb12
3
+ size 284702
requirements.txt ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ -i https://pypi.org/simple
2
+ aiofiles==23.2.1; python_version >= '3.7'
3
+ annotated-types==0.7.0; python_version >= '3.8'
4
+ anyio==4.8.0; python_version >= '3.9'
5
+ certifi==2024.12.14; python_version >= '3.6'
6
+ charset-normalizer==3.4.1; python_version >= '3.7'
7
+ click==8.1.8; python_version >= '3.7'
8
+ distro==1.9.0; python_version >= '3.6'
9
+ elevenlabs==1.50.3; python_version >= '3.8' and python_version < '4.0'
10
+ fastapi==0.115.6; python_version >= '3.8'
11
+ ffmpy==0.5.0; python_version >= '3.8' and python_version < '4.0'
12
+ filelock==3.16.1; python_version >= '3.8'
13
+ fsspec==2024.12.0; python_version >= '3.8'
14
+ gradio==5.12.0; python_version >= '3.10'
15
+ gradio-client==1.5.4; python_version >= '3.10'
16
+ groq==0.15.0; python_version >= '3.8'
17
+ gtts==2.5.4; python_version >= '3.7'
18
+ h11==0.14.0; python_version >= '3.7'
19
+ httpcore==1.0.7; python_version >= '3.8'
20
+ httpx==0.28.1; python_version >= '3.8'
21
+ huggingface-hub==0.27.1; python_full_version >= '3.8.0'
22
+ idna==3.10; python_version >= '3.6'
23
+ jinja2==3.1.5; python_version >= '3.7'
24
+ markdown-it-py==3.0.0; python_version >= '3.8'
25
+ markupsafe==2.1.5; python_version >= '3.7'
26
+ mdurl==0.1.2; python_version >= '3.7'
27
+ numpy==2.2.1; python_version >= '3.10'
28
+ orjson==3.10.14; python_version >= '3.8'
29
+ packaging==24.2; python_version >= '3.8'
30
+ pandas==2.2.3; python_version >= '3.9'
31
+ pillow==11.1.0; python_version >= '3.9'
32
+ pyaudio==0.2.14
33
+ pydantic==2.10.5; python_version >= '3.8'
34
+ pydantic-core==2.27.2; python_version >= '3.8'
35
+ pydub==0.25.1
36
+ pygments==2.19.1; python_version >= '3.8'
37
+ python-dateutil==2.9.0.post0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
38
+ python-multipart==0.0.20; python_version >= '3.8'
39
+ pytz==2024.2
40
+ pyyaml==6.0.2; python_version >= '3.8'
41
+ requests==2.32.3; python_version >= '3.8'
42
+ rich==13.9.4; python_full_version >= '3.8.0'
43
+ ruff==0.9.1; sys_platform != 'emscripten'
44
+ safehttpx==0.1.6; python_version >= '3.10'
45
+ semantic-version==2.10.0; python_version >= '2.7'
46
+ shellingham==1.5.4; python_version >= '3.7'
47
+ six==1.17.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
48
+ sniffio==1.3.1; python_version >= '3.7'
49
+ speechrecognition==3.13.0; python_version >= '3.9'
50
+ starlette==0.41.3; sys_platform != 'emscripten'
51
+ tomlkit==0.13.2; python_version >= '3.8'
52
+ tqdm==4.67.1; python_version >= '3.7'
53
+ typer==0.15.1; sys_platform != 'emscripten'
54
+ typing-extensions==4.12.2; python_version >= '3.8'
55
+ tzdata==2024.2; python_version >= '2'
56
+ urllib3==2.3.0; python_version >= '3.9'
57
+ uvicorn==0.34.0; sys_platform != 'emscripten'
58
+ websockets==14.1; python_version >= '3.9'
59
+ python-dotenv
tempCodeRunnerFile.py ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # if you dont use pipenv uncomment the following:
2
+ # from dotenv import load_dotenv
3
+ # load_dotenv()
4
+
5
+ #VoiceBot UI with Gradio
6
+ import os
7
+ import gradio as gr
8
+
9
+ from brain_of_the_doctor import encode_image, analyze_image_with_query
10
+ from voice_of_the_patient import record_audio, transcribe_with_groq
11
+ from voice_of_the_doctor import text_to_speech_with_gtts, text_to_speech_with_elevenlabs
12
+
13
+ #load_dotenv()
14
+
15
+ system_prompt="""You have to act as a professional doctor, i know you are not but this is for learning purpose.
16
+ What's in this image?. Do you find anything wrong with it medically?
17
+ If you make a differential, suggest some remedies for them. Donot add any numbers or special characters in
18
+ your response. Your response should be in one long paragraph. Also always answer as if you are answering to a real person.
19
+ Donot say 'In the image I see' but say 'With what I see, I think you have ....'
20
+ Dont respond as an AI model in markdown, your answer should mimic that of an actual doctor not an AI bot,
21
+ Keep your answer concise (max 2 sentences). No preamble, start your answer right away please"""
22
+
23
+
24
+ def process_inputs(audio_filepath, image_filepath):
25
+ speech_to_text_output = transcribe_with_groq(GROQ_API_KEY=os.environ.get("GROQ_API_KEY"),
26
+ audio_filepath=audio_filepath,
27
+ stt_model="whisper-large-v3")
28
+
29
+ # Handle the image input
30
+ if image_filepath:
31
+ doctor_response = analyze_image_with_query(query=system_prompt+speech_to_text_output, encoded_image=encode_image(image_filepath), model="meta-llama/llama-4-scout-17b-16e-instruct") #model="meta-llama/llama-4-maverick-17b-128e-instruct")
32
+ else:
33
+ doctor_response = "No image provided for me to analyze"
34
+
35
+ voice_of_doctor = text_to_speech_with_gtts(input_text=doctor_response, output_filepath="final.mp3")
36
+
37
+ return speech_to_text_output, doctor_response, voice_of_doctor
38
+
39
+
40
+ # Create the interface
41
+ iface = gr.Interface(
42
+ fn=process_inputs,
43
+ inputs=[
44
+ gr.Audio(sources=["microphone"], type="filepath"),
45
+ gr.Image(type="filepath")
46
+ ],
47
+ outputs=[
48
+ gr.Textbox(label="Speech to Text"),
49
+ gr.Textbox(label="Doctor's Response"),
50
+ gr.Audio("Temp.mp3")
51
+ ],
52
+ title="AI Doctor with Vision and Voice"
53
+ )
54
+
55
+ iface.launch(debug=True)
56
+
57
+ #http://127.0.0.1:7860
voice_of_the_doctor.py ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # if you dont use pipenv uncomment the following:
2
+ # from dotenv import load_dotenv
3
+ # load_dotenv()
4
+
5
+ #Step1a: Setup Text to Speech–TTS–model with gTTS
6
+ import os
7
+ from gtts import gTTS
8
+
9
+ def text_to_speech_with_gtts_old(input_text, output_filepath):
10
+ language="en"
11
+
12
+ audioobj= gTTS(
13
+ text=input_text,
14
+ lang=language,
15
+ slow=False
16
+ )
17
+ audioobj.save(output_filepath)
18
+
19
+
20
+ input_text="Hi this is Ai with nayan, im little bit worried!"
21
+ text_to_speech_with_gtts_old(input_text=input_text, output_filepath="gtts_testing.mp3")
22
+
23
+ #Step1b: Setup Text to Speech–TTS–model with ElevenLabs
24
+ import elevenlabs
25
+ from elevenlabs.client import ElevenLabs
26
+
27
+ ELEVENLABS_API_KEY=os.environ.get("ELEVEN_API_KEY")
28
+
29
+ def text_to_speech_with_elevenlabs_old(input_text, output_filepath):
30
+ client=ElevenLabs(api_key=ELEVENLABS_API_KEY)
31
+ audio=client.generate(
32
+ text= input_text,
33
+ voice= "Aria",
34
+ output_format= "mp3_22050_32",
35
+ model= "eleven_turbo_v2"
36
+ )
37
+ elevenlabs.save(audio, output_filepath)
38
+
39
+ #text_to_speech_with_elevenlabs_old(input_text, output_filepath="elevenlabs_testing.mp3")
40
+
41
+ #Step2: Use Model for Text output to Voice
42
+
43
+ import subprocess
44
+ import platform
45
+
46
+ def text_to_speech_with_gtts(input_text, output_filepath):
47
+ language="en"
48
+
49
+ audioobj= gTTS(
50
+ text=input_text,
51
+ lang=language,
52
+ slow=False
53
+ )
54
+ audioobj.save(output_filepath)
55
+ os_name = platform.system()
56
+ try:
57
+ if os_name == "Darwin": # macOS
58
+ subprocess.run(['afplay', output_filepath])
59
+ elif os_name == "Windows": # Windows
60
+ subprocess.run(['powershell', '-c', f'(New-Object Media.SoundPlayer "{output_filepath}").PlaySync();'])
61
+ elif os_name == "Linux": # Linux
62
+ subprocess.run(['aplay', output_filepath]) # Alternative: use 'mpg123' or 'ffplay'
63
+ else:
64
+ raise OSError("Unsupported operating system")
65
+ except Exception as e:
66
+ print(f"An error occurred while trying to play the audio: {e}")
67
+
68
+
69
+ input_text="Hi this is Ai with nayan ,im little bit worried"
70
+ text_to_speech_with_gtts(input_text=input_text, output_filepath="gtts_testing_autoplay.mp3")
71
+
72
+
73
+ def text_to_speech_with_elevenlabs(input_text, output_filepath):
74
+ client=ElevenLabs(api_key=ELEVENLABS_API_KEY)
75
+ audio=client.generate(
76
+ text= input_text,
77
+ voice= "Aria",
78
+ output_format= "mp3_22050_32",
79
+ model= "eleven_turbo_v2"
80
+ )
81
+ elevenlabs.save(audio, output_filepath)
82
+ os_name = platform.system()
83
+ try:
84
+ if os_name == "Darwin": # macOS
85
+ subprocess.run(['afplay', output_filepath])
86
+ elif os_name == "Windows": # Windows
87
+ subprocess.run(['powershell', '-c', f'(New-Object Media.SoundPlayer "{output_filepath}").PlaySync();'])
88
+ elif os_name == "Linux": # Linux
89
+ subprocess.run(['aplay', output_filepath]) # Alternative: use 'mpg123' or 'ffplay'
90
+ else:
91
+ raise OSError("Unsupported operating system")
92
+ except Exception as e:
93
+ print(f"An error occurred while trying to play the audio: {e}")
94
+
95
+ #text_to_speech_with_elevenlabs(input_text, output_filepath="elevenlabs_testing_autoplay.mp3")
voice_of_the_patient.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # if you dont use pipenv uncomment the following:
2
+ # from dotenv import load_dotenv
3
+ # load_dotenv()
4
+
5
+ #Step1: Setup Audio recorder (ffmpeg & portaudio)
6
+ # ffmpeg, portaudio, pyaudio
7
+ import logging
8
+ import speech_recognition as sr
9
+ from pydub import AudioSegment
10
+ from io import BytesIO
11
+
12
+ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
13
+
14
+ def record_audio(file_path, timeout=20, phrase_time_limit=None):
15
+ """
16
+ Simplified function to record audio from the microphone and save it as an MP3 file.
17
+
18
+ Args:
19
+ file_path (str): Path to save the recorded audio file.
20
+ timeout (int): Maximum time to wait for a phrase to start (in seconds).
21
+ phrase_time_limit (int): Maximum time for the phrase to be recorded (in seconds).
22
+ """
23
+ recognizer = sr.Recognizer()
24
+
25
+ try:
26
+ with sr.Microphone() as source:
27
+ logging.info("Adjusting for ambient noise...")
28
+ recognizer.adjust_for_ambient_noise(source, duration=1)
29
+ logging.info("Start speaking now...")
30
+
31
+ # Record the audio
32
+ audio_data = recognizer.listen(source, timeout=timeout, phrase_time_limit=phrase_time_limit)
33
+ logging.info("Recording complete.")
34
+
35
+ # Convert the recorded audio to an MP3 file
36
+ wav_data = audio_data.get_wav_data()
37
+ audio_segment = AudioSegment.from_wav(BytesIO(wav_data))
38
+ audio_segment.export(file_path, format="mp3", bitrate="128k")
39
+
40
+ logging.info(f"Audio saved to {file_path}")
41
+
42
+ except Exception as e:
43
+ logging.error(f"An error occurred: {e}")
44
+
45
+ audio_filepath = "patient_voice_test_for_patient.mp3"
46
+ record_audio(file_path=audio_filepath) # NOW ENABLED ✅
47
+
48
+ #Step2: Setup Speech to text – STT – model for transcription
49
+ import os
50
+ from groq import Groq
51
+
52
+ GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
53
+ stt_model = "whisper-large-v3"
54
+
55
+ def transcribe_with_groq(stt_model, audio_filepath, GROQ_API_KEY):
56
+ client = Groq(api_key=GROQ_API_KEY)
57
+
58
+ audio_file = open(audio_filepath, "rb")
59
+ transcription = client.audio.transcriptions.create(
60
+ model=stt_model,
61
+ file=audio_file,
62
+ language="en"
63
+ )
64
+ return transcription.text
65
+
66
+ # Transcribe the recorded audio and print the text
67
+ transcribed_text = transcribe_with_groq(stt_model, audio_filepath, GROQ_API_KEY)
68
+ print("\n=== PATIENT SPEECH TO TEXT ===")
69
+ print(transcribed_text)